{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "来自[课程2-机器学习入门实践-鸢尾花分类](https://aistudio.baidu.com/aistudio/projectdetail/78918)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **任务描述：**\n",
    "\n",
    "构建一个模型，根据鸢尾花的花萼和花瓣大小将其分为三种不同的品种。\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/dd74666475b549fcae99ac2aff67488f015cdd76569d4d208909983bcf40fe3c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# **数据集**\n",
    "总共包含150行数据\n",
    "\n",
    "每一行数据由 4 个特征值及一个目标值组成。\n",
    "\n",
    "4 个特征值分别为：萼片长度、萼片宽度、花瓣长度、花瓣宽度\n",
    "\n",
    "目标值为三种不同类别的鸢尾花，分别为：\tIris Setosa、Iris Versicolour、Iris Virginica\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/8bdc417331ef45d5a380d2769f3a8bcd7b361212b20d4e78b2a32ee9c7a7b1fa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**首先导入必要的包：**\n",
    "\n",
    "**numpy**：python第三方库，用于科学计算\n",
    "\n",
    "**matplotlib**:python第三方库，主要用于进行可视化\n",
    "\n",
    "**sklearn**:python的重要机器学习库，其中封装了大量的机器学习算法，如：分类、回归、降维以及聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                \n",
    "from matplotlib import colors     \n",
    "from sklearn import svm            \n",
    "from sklearn.svm import SVC\n",
    "from sklearn import model_selection\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Step1.数据准备**\n",
    "\n",
    "(1)从指定路径下加载数据\n",
    "\n",
    "(2)对加载的数据进行数据分割，x_train,x_test,y_train,y_test分别表示训练集特征、训练集标签、测试集特征、测试集标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#*************将字符串转为整型，便于数据加载***********************\n",
    "#在函数中建立一个对应字典就可以了，输入字符串，输出字符串对应的数字。\n",
    "def iris_type(s):\n",
    "#     print(type(s))\n",
    "#字符串加个b是指btypes 字节串类型\n",
    "    it = {b'Iris-setosa':0, b'Iris-versicolor':1, b'Iris-virginica':2}\n",
    "    return it[s]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#加载数据\n",
    "data_path='./iris.data'          #数据文件的路径\n",
    "data = np.loadtxt(data_path,                                #数据文件路径\n",
    "                  dtype=float,                              #数据类型\n",
    "                  delimiter=',',                            #数据分隔符\n",
    "                  converters={4:iris_type})                 #将第5列使用函数iris_type进行转换\n",
    "# print(data)                                                 #data为二维数组，data.shape=(150, 5)\n",
    "# print(data.shape)\n",
    "#数据分割\n",
    "x, y = np.split(data,                                       #要切分的数组\n",
    "                (4,),                                       #沿轴切分的位置，第5列开始往后为y\n",
    "                axis=1)                                     #1代表纵向分割，按列分割\n",
    "\n",
    "x = x[:, 0:2] \n",
    "#第一个逗号之前表示行，只有冒号表示所有行，第二个冒号0:2表是0,1两列\n",
    "#在X中我们取前两列作为特征，为了后面的可视化，原始的四维不好画图。x[:,0:4]代表第一维(行)全取，第二维(列)取0~2\n",
    "# print(x)\n",
    "x_train,x_test,y_train,y_test=model_selection.train_test_split(x,              #所要划分的样本特征集\n",
    "                                                               y,              #所要划分的样本结果\n",
    "                                                               random_state=1, #随机数种子确保产生的随机数组相同\n",
    "                                                               test_size=0.3)  #测试样本占比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "random_state=1确保了每次运行程序时用的随机数都是一样的，也就是每次重新运行后所划分的训练集和测试集的样本都是一致的，相当于只在第一次运行的时候进行随机划分。如果不设置的话，每次重新运行的种子不一样，产生的随机数也不一样就会导致每次随机生成的训练集和测试集不一致。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Step2.模型搭建**\n",
    "\n",
    "C越大，相当于惩罚松弛变量，希望松弛变量接近0，即对误分类的惩罚增大，趋向于对训练集全分对的情况，这样对训练集测试时准确率很高，但泛化能力弱。\n",
    "C值小，对误分类的惩罚减小，允许容错，将他们当成噪声点，泛化能力较强。\n",
    "\n",
    "kernel='linear'时，为线性核\n",
    "\n",
    "decision_function_shape='ovr'时，为one v rest，即一个类别与其他类别进行划分，\n",
    "\n",
    "decision_function_shape='ovo'时，为one v one，即将类别两两之间进行划分，用二分类的方法模拟多分类的结果。\n",
    "ovr和ovo是分类是三种多类情况中的两种情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#**********************SVM分类器构建*************************\n",
    "def classifier():\n",
    "    #clf = svm.SVC(C=0.8,kernel='rbf', gamma=50,decision_function_shape='ovr')\n",
    "    clf = svm.SVC(C=0.5,                         #误差项惩罚系数,默认值是1\n",
    "                  kernel='linear',               #线性核 kenrel=\"rbf\":高斯核\n",
    "                  decision_function_shape='ovr') #决策函数\n",
    "    return clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.定义模型：SVM模型定义\n",
    "clf = classifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Step3.模型训练**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#***********************训练模型*****************************\n",
    "def train(clf,x_train,y_train):\n",
    "    clf.fit(x_train,         #训练集特征向量，fit表示输入数据开始拟合\n",
    "            y_train.ravel()) #训练集目标值 ravel()扁平化，将原来的二维数组转换为一维数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.训练SVM模型\n",
    "train(clf,x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Step4.模型评估**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#**************并判断a b是否相等，计算acc的均值*************\n",
    "def show_accuracy(a, b, tip):\n",
    "    acc = a.ravel() == b.ravel()\n",
    "    print('%s Accuracy:%.3f' %(tip, np.mean(acc)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_accuracy(clf,x_train,y_train,x_test,y_test):\n",
    "    #分别打印训练集和测试集的准确率  score(x_train,y_train):表示输出x_train,y_train在模型上的准确率\n",
    "    print('trianing prediction:%.3f' %(clf.score(x_train, y_train)))\n",
    "    print('test data prediction:%.3f' %(clf.score(x_test, y_test)))\n",
    "    #原始结果与预测结果进行对比   predict()表示对x_train样本进行预测，返回样本类别\n",
    "    show_accuracy(clf.predict(x_train), y_train, 'traing data')\n",
    "    show_accuracy(clf.predict(x_test), y_test, 'testing data')\n",
    "    #计算决策函数的值，表示x到各分割平面的距离,3类，所以有3个决策函数，不同的多类情况有不同的决策函数？\n",
    "    print('decision_function:\\n', clf.decision_function(x_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trianing prediction:0.819\n",
      "test data prediction:0.778\n",
      "traing data Accuracy:0.819\n",
      "testing data Accuracy:0.778\n",
      "decision_function:\n",
      " [[-0.5         1.20887337  2.29112663]\n",
      " [ 2.06328814 -0.0769677   1.01367956]\n",
      " [ 2.16674973  0.91702835 -0.08377808]\n",
      " [ 2.11427813  0.99765248 -0.11193061]\n",
      " [ 0.9925538   2.06392138 -0.05647518]\n",
      " [ 2.11742969  0.95255534 -0.06998503]\n",
      " [ 2.05615004 -0.041847    0.98569697]\n",
      " [-0.31866596  1.02685964  2.29180632]\n",
      " [-0.27166251  1.09150338  2.18015913]\n",
      " [-0.37827567  1.14260447  2.2356712 ]\n",
      " [-0.22150749  1.11104997  2.11045752]\n",
      " [-0.18331208  2.10066724  1.08264485]\n",
      " [-0.05444966  0.99927764  2.05517201]\n",
      " [-0.46977766  1.17853774  2.29123992]\n",
      " [-0.05760122  2.04437478  1.01322644]\n",
      " [ 2.1747228   0.93698124 -0.11170404]\n",
      " [-0.13315707  2.12021384  1.01294323]\n",
      " [-0.21752096  2.12102642  1.09649454]\n",
      " [ 2.11427813  0.99765248 -0.11193061]\n",
      " [ 2.16359817  0.96212549 -0.12572366]\n",
      " [-0.21038286  1.08590572  2.12447714]\n",
      " [ 2.21291822  0.9265985  -0.13951672]\n",
      " [-0.13399204  1.06514025  2.06885179]\n",
      " [-0.18016052  1.0555701   2.12459042]\n",
      " [-0.2334671   1.08112064  2.15234646]\n",
      " [-0.08782356  2.0747104   1.01311315]\n",
      " [-0.20324476  1.05078502  2.15245974]\n",
      " [-0.11489433  1.05994888  2.05494545]\n",
      " [ 2.17787437 -0.1081159   0.93024154]\n",
      " [-0.23578369  2.18129137  1.05449232]\n",
      " [-0.20639632  1.09588216  2.11051416]\n",
      " [-0.21038286  1.08590572  2.12447714]\n",
      " [-0.02969547  2.11420989  0.91548558]\n",
      " [-0.12685394  1.03001955  2.09683439]\n",
      " [-0.09496166  2.1098311   0.98513056]\n",
      " [ 2.10547008 -0.07737399  0.97190391]\n",
      " [ 2.11029159  0.98767604 -0.09796763]\n",
      " [ 2.20411017 -0.14842797  0.9443178 ]\n",
      " [-0.20324476  1.05078502  2.15245974]\n",
      " [ 2.19066895  0.97688701 -0.16755596]\n",
      " [-0.16022784  2.10545232  1.05477553]\n",
      " [-0.23661866  1.12621778  2.11040088]\n",
      " [-0.09579663  2.05475752  1.04103911]\n",
      " [ 2.11344315 -0.05742111  0.94397795]\n",
      " [ 2.10231852  0.96772315 -0.07004167]\n",
      " [-0.12203243  2.09506958  1.02696285]\n",
      " [ 2.11029159  0.98767604 -0.09796763]\n",
      " [-0.41248455  1.16296364  2.2495209 ]\n",
      " [-0.16820091  1.08549943  2.08270149]\n",
      " [-0.42045762  1.14301076  2.27744686]\n",
      " [-0.24857827  1.09628845  2.15228982]\n",
      " [-0.27796564  2.18169766  1.09626798]\n",
      " [-0.09264507  1.00966038  2.08298469]\n",
      " [-0.25339978  1.03123843  2.22216135]\n",
      " [-0.05361468  2.05435123  0.99926346]\n",
      " [ 2.15395516 -0.16797456  1.01401941]\n",
      " [-0.12203243  2.09506958  1.02696285]\n",
      " [ 2.06579305  1.08825305 -0.15404611]\n",
      " [-0.11007283  2.12499891  0.98507392]\n",
      " [-0.27166251  1.09150338  2.18015913]\n",
      " [ 2.13652739  0.94736397 -0.08389137]\n",
      " [-0.29789831  1.13181544  2.16608287]\n",
      " [ 2.15163856  0.93219616 -0.08383473]\n",
      " [ 2.1747228   0.93698124 -0.11170404]\n",
      " [-0.11174277  1.01485174  2.09689103]\n",
      " [-0.06872585  2.06951904  0.99920682]\n",
      " [-0.23745364  1.0711442   2.16630944]\n",
      " [ 2.12141623  0.96253178 -0.08394801]\n",
      " [ 2.1627632  -0.09294809  0.93018489]\n",
      " [-0.06557429  1.0244219   2.04115239]\n",
      " [ 2.16758471  0.97210193 -0.13968664]\n",
      " [-0.12203243  2.09506958  1.02696285]\n",
      " [ 2.1293893   0.98248467 -0.11187396]\n",
      " [-0.21038286  1.08590572  2.12447714]\n",
      " [ 2.01962457  1.0786829  -0.09830747]\n",
      " [ 2.18269588  0.95693412 -0.13963   ]\n",
      " [-0.16106282  1.05037873  2.11068408]\n",
      " [ 2.20976665  0.97169564 -0.1814623 ]\n",
      " [-0.03850351  2.03918342  0.9993201 ]\n",
      " [ 2.17555778  0.99205482 -0.1676126 ]\n",
      " [-0.11007283  2.12499891  0.98507392]\n",
      " [-0.07502898  2.15971332  0.91531566]\n",
      " [ 2.13254086  0.93738753 -0.06992839]\n",
      " [ 2.09518042  1.00284385 -0.09802427]\n",
      " [ 1.0045134   2.09385071 -0.09836411]\n",
      " [ 2.24314055  0.89626288 -0.13940344]\n",
      " [-0.09579663  2.05475752  1.04103911]\n",
      " [-0.14910321  1.08030806  2.06879515]\n",
      " [ 2.13652739  0.94736397 -0.08389137]\n",
      " [-0.2334671   1.08112064  2.15234646]\n",
      " [-0.07271239  2.05954259  1.0131698 ]\n",
      " [-0.2739791   2.1916741   1.082305  ]\n",
      " [-0.27564905  1.08152693  2.19412211]\n",
      " [-0.12203243  2.09506958  1.02696285]\n",
      " [ 2.06013657 -0.03187056  0.97173399]\n",
      " [ 2.07608272  1.00803521 -0.08411793]\n",
      " [-0.19443672  2.12581149  1.06862523]\n",
      " [-0.16421438  2.09547587  1.06873851]\n",
      " [-0.3440668   1.12224529  2.22182151]\n",
      " [-0.1180459   2.10504603  1.01299987]\n",
      " [-0.20240979  1.10585861  2.09655118]\n",
      " [-0.17617399  1.06554654  2.11062744]\n",
      " [-0.2477433   2.15136204  1.09638126]\n",
      " [-0.2334671   1.08112064  2.15234646]\n",
      " [ 2.11029159  0.98767604 -0.09796763]]\n"
     ]
    }
   ],
   "source": [
    "# 4.模型评估\n",
    "print_accuracy(clf,x_train,y_train,x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Step5.模型使用**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw(clf, x):\n",
    "    iris_feature = 'sepal length', 'sepal width', 'petal lenght', 'petal width'\n",
    "    # 开始画图\n",
    "    x1_min, x1_max = x[:, 0].min(), x[:, 0].max()               #第0列的范围\n",
    "    x2_min, x2_max = x[:, 1].min(), x[:, 1].max()               #第1列的范围\n",
    "    x1, x2 = np.mgrid[x1_min:x1_max:200j, x2_min:x2_max:200j]   #生成网格采样点 开始坐标：结束坐标（不包括）：步长\n",
    "    #flat将二维数组转换成1个1维的迭代器，然后把x1和x2的所有可能值给匹配成为样本点\n",
    "    grid_test = np.stack((x1.flat, x2.flat), axis=1)            #stack():沿着新的轴加入一系列数组，竖着（按列）增加两个数组\n",
    "    print('grid_test:\\n', grid_test)\n",
    "    # 输出样本到决策面的距离\n",
    "    z = clf.decision_function(grid_test)\n",
    "    print('the distance to decision plane:\\n', z)\n",
    "    \n",
    "    grid_hat = clf.predict(grid_test)                           # 预测分类值 得到【0,0.。。。2,2,2】\n",
    "    print('grid_hat:\\n', grid_hat)  \n",
    "    grid_hat = grid_hat.reshape(x1.shape)                       # reshape grid_hat和x1形状一致\n",
    "                                                                #若3*3矩阵e，则e.shape()为3*3,表示3行3列   \n",
    "  \t#light是网格测试点的配色，相当于背景\n",
    "    #dark是样本点的配色\n",
    "    cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])\n",
    "    cm_dark = mpl.colors.ListedColormap(['g', 'b', 'r'])\n",
    "     #画出所有网格样本点被判断为的分类，作为背景\n",
    "    plt.pcolormesh(x1, x2, grid_hat, cmap=cm_light)                                   # pcolormesh(x,y,z,cmap)这里参数代入\n",
    "                                                                                      # x1，x2，grid_hat，cmap=cm_light绘制的是背景。\n",
    "    #squeeze()把y的个数为1的维度去掉，也就是变成一维。\n",
    "    plt.scatter(x[:, 0], x[:, 1], c=np.squeeze(y), edgecolor='k', s=50, cmap=cm_dark) # 样本点\n",
    "    plt.scatter(x_test[:, 0], x_test[:, 1], s=200, facecolor='yellow', zorder=10, marker='+')       # 测试点\n",
    "    plt.xlabel(iris_feature[0], fontsize=20)\n",
    "    plt.ylabel(iris_feature[1], fontsize=20)\n",
    "    plt.xlim(x1_min, x1_max)\n",
    "    plt.ylim(x2_min, x2_max)\n",
    "    plt.title('svm in iris data classification', fontsize=30)\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid_test:\n",
      " [[4.3       2.       ]\n",
      " [4.3       2.0120603]\n",
      " [4.3       2.0241206]\n",
      " ...\n",
      " [7.9       4.3758794]\n",
      " [7.9       4.3879397]\n",
      " [7.9       4.4      ]]\n",
      "the distance to decision plane:\n",
      " [[ 2.04663576  1.0980928  -0.14472856]\n",
      " [ 2.04808477  1.09663836 -0.14472313]\n",
      " [ 2.04953377  1.09518392 -0.1447177 ]\n",
      " ...\n",
      " [-0.21454554  0.96016146  2.25438408]\n",
      " [-0.21309653  0.95870702  2.25438951]\n",
      " [-0.21164753  0.95725258  2.25439495]]\n",
      "grid_hat:\n",
      " [0. 0. 0. ... 2. 2. 2.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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QZZkdh9y2c/QcSSkdXN1LDuVydGke9SZgUi7O96ncgSdQvemscB928NWcmRXz3ba9Wn8MC+JzXs7LE7JtCA0PrSFCCK8hsYQQtUj3+kzEy5eJMffjO+PfmiiPrjQT0Vxvc0M0BQMhxBghxG3Z9DiedNventM8pAq7l9YbsABzDTf0jLrcjXq+suIPt+3HPH2RGyb8+fj20XjQ+Ht9Nr2MtMDvAN0MR7SM+VZArYCeneOAr4xDObAAvC2E6O0toRAi1pNO2RCQsjTyfj2r3pEQohiqV5fGdrdj9wshHs6q/IUQLUgPz3fAmHsWKj4g3RR4r+EV67FhFkJECSE6Zti92W17jBAiIsNxhBB3Aa/4oMtBt+1GPqTPji9Q4Q8B7hNCPJMxgdEIvkv6/dgspcxyWlQw8OVLKBa1asNUIcQq1EoPadHNy6HG93qT/gXxlpQyKw/EWaQPbk/OsF9TsGmPihryr+HOvQ1lgjKhpj10RTkugBrjeSOX+bwG9ETFimwE/C2E+AQ1t9Q91uglQ4c4T0KklMeFEN+jJvCWQrnZf4Ry/09zqHgI5YL9pbGdFStQruvRwCIhxEyUq3eaWXKTlPK8lNJm5DMMNU61RgjxGapSsxvX9LChky/5ZouUcoMQYgRqTCwSmGNUTGkrjZhQY0itUeNOk8mBiSqAZRmB6h08J4TYjPKm3YUKt1YSFdf4PtKnHvyGMv2mURu1usk7QohfUGV6FPW8lTeu727SLQXudVCeI6W8JITog5r6EInyiO0hVIzmXSgHwoqoevYu1PO83E3EAtRYYyVUcIK/hRCfouroUqgFhO9C1ddp98ebLheEEH+iGqV2QogPUc/0Zbc03mYFeJJ3WQjxMOoZM6M+wO5BebmeQY3XPoh6ZzDy8ftZzxU+hNHxNbK5C9VgZrfChEDdJPdzfQqFQ4ZlmLJIF+cmO8tQQfgQTslH3XISzizLfPKjTr7IRE2b8OVZOQN0zO11GXkFavWJsqjK2pscJyriR7bPFKoyOp2FrDi3tJE+lNeHKItJ2v9f+FNmRr6P4dvqIGM8nBufdtyLbL/LEtUY+/IMSaP8ymY4f4yP59qAEbl9L7IrC7d04zzdfw/pmnN1CEmv1+zh3BaoRQeyehc6+6ILajqc1xVEMqTt73asfxbX1g31IZPVdR0GGmUhI9t74k+95ou5ZxZqxYBhKFPNPpRZx0n6F/e7QGMp5VCZTdQKqTT9KsPu7CLJaAoGd6J6Y6+hvtL/RfVw0taRW4EK/ltbSrncmxBfkMq9vjlqpYINpHup7UF5MTeQUv7qg5xzqIpkJMqjNdn47Qc+B1pKKSf6qNNxVG/ubVTg6LTg0p7SpqIcBJ5EBai4jOq1HEZNIeokpRxEgOdMSSlnoBrX0ajx1jOoii8JNbn/K5SFxxdTWkbZfpellPIwqqfwMKrnuANVzzgNWf+gVr+5S0rZ3sjTnUmGDqNQEWwOoebIOlANwiZUuK8bpZRew37lNVLKjagVIgaifCVOoN6btGdiEWrtzZ4ezt2AqqPfRZW1DVVmO1HXerOU8ueM53nRYwnK+egblKnU7/nFUsqFqKGwMaSvHWtHfTSuBIYA18vso88EDWG0oBqNRqPRFEmC4Uat0Wg0Gk2BQTeEGo1GoynS6IZQo9FoNEUa3RBqNBqNpkgTyogKeUrxcsVluWr+r0VpSjLhis6Pi0dkT0HVPbd6W5Os/HvgX2RZDw5hDjAnmKlSr0oANPROvi/zc2U97jaZknC5AhFcJO8pqLrnR70PHdpyVkqZXUSaAk+RaQjLVSvHuD/G+S0nJj6GxLhgrmQTPAqq7rnVO+lCEkOvG4r9AXvmgFEboX5UfYbOHxoYJb2Q78t8Zj+Pu2Ni4klMjMtbXQJEQdU9P+rdv784HGod8gJtGtUUWqJLR9O8b3PCl4anr4QHcAosGy3cOTwQIS81Gk1Bp8j0CDVFk4feeYikh5LY+e5OTLVMiBSB65iLfu/1o1YLX1ZxKqR46QlqNEUR3RBqCjWWSAtD5g7h33/+5Z+1/xAZE8l/7vgPEdGZYhNrNJoiim4INUWCirUrUrF2xVCrEXp0T1CjyYQeI9RoNBpNkUY3hBqNRqMp0uiGUKPRaDRFGt0QajRFBT0+qNF4RDeEGo1GoynSaK9RTb6mE1MwNzDzMy+EWpWCi+4JajRZonuEGo1GoynS6B6hRlNY0T1BjcYndI9Qk285te8UZw+dw251sHXhVpwOZ6hV0mg0hRDdEGryJQsmLOClxi9x7vxZnE4HHw/7mJE3j+TSqUuhVq1goHuDGo3P6IZQk+/YsWwHS95bgn2gHVkGMEPqg6mcLXeWDx76INTqaTSaQoYeI9TkCzox5cr2DbFHufNXGxSHuGoAB1jVH+jnQhzbQ43UlwmPVI/uMkaGQNt8jO4JajQ5RvcINfkOe6oNLB4OCBAWgd1q93BQo9FocofuEWryBe49u9cnvs5OuROawKp+UDOqBu1mHgAbhL9jZuquZyldsnQItc2H6J6gRpNrdI9Qk+/oPLQzlo0WcPeLkWBebaZum7qUrqQbQY1GEzh0Q6jJd9zY/ka6De9G+IxwxDkBToj8LJLYlFgGzRwUavU0Gk0hQ5tGNfmSLsO70PK+llzjmkKYOYzBnw3mxltvRAgRatXyH9osqtH4hW4INT6TeD6Rzd9t5vLZy1RrVI16HethMgfPqFC6UmnKUBrzRTM3dbgpaPloNJqijW4INT6xfvZ6Pn/8c6gF9hg7EZ9GUDyiOCN/GUmZymVCrZ5Go9HkGt0QarLlxK4TfP7E59gesEEFtS+VVGy/2Xiz+5u8vOnloOW9jJHEbIuBuMSg5VFg0SZRjSYgaGcZTbYsf3c5joaOK41gGq5bXJw6dIrD2w6HRjGNRqMJALpHqMmWY7uP4Yp1ZT5gAlMlE6f2nqJqg6p5r1hRRfcENZqAonuEmmypWKMi4rQHb00J8pSkXLVyea+URqPRBAjdI9Rky21P3samWzdhq2cDt7nsYougVOlSVG9aPXTKFSV0T1CjCQq6IdRkS9WGVek1oRdzR83FVc+Fs7iTiGMRRFyK4NmVzxaquX1pwb91MG+NpuigG0KNT9z29G00vKsha2et5dLpS9R8oCZNezXFEukpOrYm4OjeoEYTNHRDqPGZclXL0e2lbqFWI2gknE7govMS0iU5mXiS2LqxoVZJo9HkAfnSWUYIYRZC/CmEWOzhWIQQYo4QYp8QYqMQolrea6gpbCx6dRHDagzj9PFTnP73NGOaj+Gdvu/gsDlCrZpGowky+bIhBIYAu7wcexS4IKWsBbwJvJpnWmkKJVsXbmXRm4twDHTgKieRZSX2p+3s2LGDuaPmhla5mf20WVSjCTL5riEUQlQGugAzvCTpBsw0tucBt4rC5K2hyXMWvroQWxsblHTbGQ62jjbiP4nHlmILmW4ajSb45McxwreAF4DiXo5XAo4CSCkdQohLQFngbN6opykMpHmHAlSfegRXLGCGuGpq3yqjE2bqaacar2AhHMhDb1LdC9Ro8ox81RAKIe4ETksptwgh4rwl87BPepE3EBgIUK5COWLiY/zW0ZxoDoicUFBQdQ+G3uYG5ivbNarXQJql8WQdAKBmVA118DpBRGoEwqr+jdmWMz1yrXtMfM7PCSBmcyIxIdYhtxRU3Quq3oWBfNUQArcAXYUQnYFIoIQQ4isp5QNuaY4BVYBjQogwlEHrvCdhUsqPgY8BqjepLhMDELg5Jj6GQMgJBQVV92Do/TMvXNn+9etfmfvWXGx9bKx6TO1r98UBzL+aaVC5AYO/HZx+Yg71yJXu+aA3GBMTT2JiXKjVyBUFVfeCqndhIF+NEUopR0opK0spqwF9gZUZGkGAH4G0mqKnkcZjj1CTv7mXJ7iXJ0KtBu0Hteemm28i4rMIuAQkQMTsCMpeKEv/d/uHWj2NRhNk8luP0CNCiAnAH1LKH4FPgVlCiH2onmDfkCqnKfCYzCae+e4Zdq/eTcmoD5FOFw+Pu58mPZoQZsnDVyQf9AQ1mqJIvm0IpZTxQLyxPcZtfyrQKzRaaQLF4W2HsdWxgRCcP3c+5Iv7CiG4Ie4GYqkIQIsbWoRUH41Gk3fkK9OopvDjcDgY1XAUY5uNxeFw4rA7GFZ9GO/0eifUqmk0miKKbgg1ecq0O6Zx4vQJGAZYjN+TsGXpFuaPnh9i7UKEnjSv0YQU3RBq8ozU5FR2rd4F9wBRbgfKALfDsveWhUizdJYxUq88odEUMfLtGKGm8JDmGeq0uOhzGjUxBihl/L0wAjUT1GqjmJsX6Ww+yFM9NRpN0UT3CDV5hslkxELwNNmlqE6A0SZRjSbk6B6hJuhc6dmZYEiDIVy69hJ0MnqCQOnJwNdwY80beWH5C17laDQaTTDQPULNFfJigvvzi5/HtM0EXwJOwAF8BJEJkTw97+mg5p2v0A4ymnxOp05xbNxI3VDrkRfohlCTp1SpV4W3Dr3F/93yf6oRdMEd/e5g+tHpFCtRLNTqaTSaIog2jWoAOHPwDNYKNpBw6t9TVKhZIWh5lShfgse/fJxibAOgz9Q+QcsrjYTTCRzYfICI6AjqtKqDOcyc/UnBQPcCNUEkIeE0Bw5sJiIimjp1WmE2566Kl1KSmlrw4hLnFt0Qanix0Ysc33Gcx4yFrEZcP4LY62OZsmNK1icWAJwOJ7OemcXaWWsJqxqGTJaYk80M+HQADbo0CLV6Gk1AcDodfDfrKdau/ZImYRbOS/jMHMb9A76kQYMuOZJ14sRuPpvejbpz9mGODJLC+QxtGi3ivNHlDY7vOw5PABHG70k4efQkU9oX/Ibw2xHfsm7lOuxP2knplUJqv1SSOifx/v3vc3jb4bxVRvcGNUFiwbfPkbruKw7ZU1mZksC21ATmJ51n5vu9OXx4m89yUlIu89bk1gz79x9auFxB1Dh/oRvCIs5fK/+CrqiljdMoA3SDPev2BDXv2XwQ1LmCKZdTWP3JamxdbOA+/FgV7M3tLH5tcdDy1mjyipSUy6xZ/Qlf25Jxj9jbGvivPZWVi33/oN2w/mua2VN4Aulx4dfCSkBMo0IIM1AXKA14HHyRUq4JRF4a/0nzDJUSup1E9QJFhgnuAG9DlGsQwpj/V9AmuJ/cfRJzWTOUyHxM1pDs+2Vf3iiie4KaIHLy5G6uM1u4lpRMx26XLt7fty7L8zt1iruyfXP9XZR/OEn9EwfNoOCt5J0L/G4IhRCjgWdRC+RmRYi8EzS5Im2Cuyi434UxZWJwXnKqaRoZn74EiClbJN5xTSEnJqYMp5w2HGSu0I8BMTFlPZzlGbPZQmoglSsg+NUQCiFeAMajljOdBRxFOcVr8jFXenYCnqjzBClVUuAuuPBftbv0q8BiiDgQwUfnClYv0J3yNctTvmZ5jm0/Bo3cDjjBstnCrcNvDZluGk2gKF++JuXK1+KzYzsZ6BaiyQ5MthSj2a1PZXn+smXxV7aPHt3B9Jebs8OWQoVVsKkYic2aBUnxfIS/PcIBwHGgkZTyTAD00eQxLyx5gfEtx8NFYLix8yvgCDy38rlcy+2EGpcIdQDrQZ8PYlK7SThOObDXskMyRGyLoOb1NWnVv1VwM5/ZD2Lig5uHRgPcP+gbRk5qxWaHlR72VM4Cb0VEI2r+H7e06u+znCpV6tPm9udpvHQaG0gOmr75DX8bwirAJ7oRLLhUb1yd13a/xjs93gH7EQAqR1fmqR1PEVsnNsTa+U/lepWZsmMKKz5YwY4VO4gqHkXbyW1p2rNp6OYSajQBpnLleoyZspvVK97jvzuWEhFVgqZtB9C0ac8czyW8q/sE6tzUgTPFe4DzbJA0zl/42xCeCoAMTYi5pvo1jNk4hgihTCjjNo0jzJL723rx34skR6YQZjFf7a3phi3FxuE/D2MON1OtUTVM5uA5MJeqWIoe43vQY3yPoOWh0YSaUqUq0q3Hy9DjZb9l1a3bhthKN3HkyOoAaJb/8bf2mQvcJoSICIQymtDw+8zfeabSM1iTrViTrQyuNJj4GfE5lpOamMo7fd/h+drPc2zPMQ5uPcSYFmM4vf/0lTRSSpZMW8LgawfzxgNvMPWeqQy5bghbftgSwCvKB2hPUY2mwOBvQzhaIP9lAAAgAElEQVQGOAnME0JUD4A+mjzmz0V/8uVzX5J8T/KVFeNTeqbwzahv2Dx/c45kvd3zbbbv247jaQeuCi5kJcnR0keZ2HYiqYnKF23VR6tY8MYCrA9YSemXQuqAVC53usxHD3/Ent+CO29Ro9FoPJGjhlAIccD9B/wPqAZ0BvYJIc5nTGP89gdBd00A+G7sd9hus8G1ylu09KtARbB1tDFv3Dyf5Rz96yj7N+/H0dlxZeFdBMgWEmtZK+u+WofL5WLBxAVqgns5t5Orgq2Nje9f/j6QlxYa9KoSmkLCsmXxNG9Okfg6zelAkInMS6g6gCNu/3uaeFZwJ6MVYlxOFye2n4A7PRysCafnnMZutRMeEe5VRpp36MVyF2m93I40pizFVVN/V/UDulspbv6eCva1LPo2EVkZ2s3MIKgOHPz4oL+XpNFoNDkmRw2hlLJakPTQhABhEoRFhuFIdkDxDAdTwBRm8tmz0mQ2IVxeFpp3gTnCjDCbwCXBUwjDJIiIKcBDzboXqNEUWLTHZxFGCEGzPs3YsGEDrtuubp1MG0007tE4W2/OtHmCKcVSGHLrEGwP2qC80RME2n0Elk8tvPDjYGrVqcUrk15hD3ugpVuTKSFsYxitHgzyvD6NRqPxgF/OMkKIlUKIh7JJ84AQYqU/+WiCR58pfSh5vCThP4bDQeAghC0Oo/jB4tw79V6f5UQVj6Lf+/2wzLYg1guwAokQMSuClr1aUqtFLQAefu9horZFEbY0DA4D+yH8+3DKWsty18i7gnKNQUf3BjWaAo2/PcI4ID6bNFWBtn7mowkSJSuUZOLWiax4fwUb5m9ASknz7s3p8GSHHMfivOWBW4itG8vPb/xMeMJ2wi1hDHz3MRp1S49vVqFWBSZvm8zyd5az9aethFnCaNW/FXED44iMKSKLn2k0mnxFXphGoyhC8UcTzydy/uh5ylQuk2+COluTrZzed5o69joej0eXjqbri13p+mJXv/Oq0bQGT89+mpqGE03jmxtnSlMqthS9J/em9+Tefuen0QSDCxeOk5h4jvLlaxIRER1qdTRBJhANoUf/CCGEAK5DTa04GoB88jXJl5L57InP2L5oO2FlwnCcd1C/c30e/fBRokuH5kVyOV1899J3rHh/BaYSJiY9N4l3J7zLwBkDia1b8MOnhRxtEi10nD69n68/epDDR/6kvNnCvy47beMGcXefqTkOVaYpOOR4jFAI4RJCOIUQTmPXuLT/3X+oXuABoAHwbQB1zndIKZl6+1S27d+G/Sk7KY+kYH/azvYj23nltldwhWil51nPzmLF/BXYHrGR+lgqsrzkUPFDTGwzkYQzCSHRSaPJryQmnuf1CS148MBG/rWnsic1gf/ZUrgc/xFzvng81OppgkhuPnHWkN4LbIOaQ3jIQzoncA5YAczIjXIFhd3xuzl57CSORxzpnxaR4Ozk5MznZ/jfL/+jfqf6eapTwpkEfvvsNxxPOdLjfQqQzSW2szZWfriSu0ffHbT8Q73qRFDRPcFCyW+rZ9DemsRzMv3DtTLwgy2ZKuu/pnOPiZQqpS0phZEc9willHFSynZSynaoifKfp/2f4ddBStlHSvmxlDI0XaI8YteqXVhrWDOXpoDUGqnsWrUrz3Xav2E/4dXCPQa9tte289evf+W5ThpNfmb/9kX0tmde5b0kcEtYBP/8k/VK75qCi79G7+qoleyKNJExkZhtZpw4Mx0z28xEROf9RPGI6AhkisfhW0ghJDoVeHRPsFBjiSzhtTK7CEREeFlKRVPg8WseoZTysJTyUqCUKag06dkE098mSMxwIBlMf5to3qd5nutUp1UdRIKAYxkOOCFiewRtH9IzWjQadxq1eZTpEdHYM+zfCuxBcsMN7UOhliYPyFGPUAgxJpf5SCml/4tk5VPK1yhPpyGd+GXGL1hbWuFa4CRErI8gbkAcFetUzHOdwixhPPrRo3z82MfYWthY9TbULP4+o8ZZqF6zOk17Ns10Tn5ZVT5fonuDhZ5GjbqxsVZL2v+zltG2ZKoByxGMt0Rx78OfEB6urSiFlZyaRsd52OdufxMe9gtju9A2hAA9J/SkRuMa/PTmT5zeeJprql9D5+mdaXxP5nl0eUWT7k0oU7kMP776I+azOxDFTPQa3It2A9vp1dk1mgyYTGYef/Yn1qyZwZBf3+Vy4jmuq9aYQV1HU6tWi1CrpwkiOW0I23nY9yxqruDXqCgz/wIVjbT3AT8Bb+VexYJDo26Nroqikh+o0awGT379JJWtE7E4w7nt6ds8pnO5XKQmW9V2MRcmk2erefKlZFIupVAytiRh4cGfV5V4PpFIeyQupyuoq9hrNABhYeG0b/8E7ds/EWpVNHlIjmoWKeVq9x8qfNptQAsp5cNSyplSymXG3/5AS6ATamK9Jo9xuVy83uV1BpYcyOHtR7AmWXmq0lPsW7/vqnRLXlvCgFIDOLTlEIe2HGJAyQEsmrToqjTnj51nWrdpPHPtM4xsNJLBsYNZOGlh0OZIntxzkskdJjO0ylCO/+84z1R5hl/f/xUpvTgABQu9vqBGU+jx9xP7WWCOlHKrp4NSyj+AuUa6bBFCRAohNgkhtgsh/ieEGO8hTX8hxBkhxDbj95hfV1CImdBqAju37IRHUZ8iFkiqk8SkdpM4c/AMACveX8Gcl+bg7OxUaa4D511O5r88nyXTlgCQkpDC+FvGszNhJ45nHNgG20jpm8JPn/3EN89/E3C9Lxy/wMutX2Zv+F4czzqQFSSJdyYyd9LcKzppNBpNoPC3IawLnMwmzQkjnS9YgfZSyptREWluF0J4Ms7PkVI2MH6FerJ+bjm1/xSHNh+CB4AKbgfiQNaUfPn0lwDMHTMXugA3oEZzBepu3QnfT1Irxq/5fA0ppVOQcTJ99flrwNbDxupPVgc8Ss3St5ZirW2FFkDamsCVwNbdxsJJC7Gl2gKan0ajKdr4O8iTANySTZpWZJ5Y4BGp7F5pacONXx7bwgo2aZ6fZ81nabYGMAJhqBXjD7CqHzR4HnDsIEo+Qbd9VoiAbacyrCovgcN26jgncUPcMQZ2sNFuXobMosFc3cze3/bSpHuTgF3DtqXbcDbLPCeTcmAqZeLItiNXlnUKGtocqtEUGYQ/Yy5CiE+B/sCbwHgp5WW3Y8VRXqZDUdFnfDJhCiHMwBagFvCelHJEhuP9gSnAGWAv8KyU0mNQbyHEQGAgQLkK5Rp/+O2HObg6z5gTzThjPFTS+YQ2DdQMF4fNicNmv9KjqlLhAABHT9XgmlInALWqvMvhAhOcuXDtVWkAsKlgAbZUOy6czI1/MlN+4rygfOXyRJWMCtg1HN91HHuUHQxv9coRlTlmVRMixRlBbK1YLMUsAcvPI+fKBkSM2ZyI05k/ViHJCQVVbyi4uudHvXv2bLdFShm4r9x8ir8NYXlgPVANuAxsA06hjHENgBKowNstpZSncyi7FLAAGCyl3Om2vyyQKKW0CiEGAb2llNnOdK3epLoc98e4nKjgkZj4GBLjfOrghpSki0k8Vf4pNT5YUfXyakbV4Lr3D8Bn0Ob2NjzyySMMqTaESzUvQRu3VeVnAmsh5u8Y3j32Lhtmb+Dz0Z9jvT9DGLkzEPFVBNNPTCeiWODmWP38+s8s+HIB9u52EPB6ndd5fu/zcBBKrCzBW4fe8urVGhAC2BuMiYknMTEuYPLyioKqNxRc3fOj3v37iyLREPobWeY00BT4FGVmbQP0Mv6GAZ8AzXPaCBqyL6KmY9yeYf85KaXV+PcTIHQT9fIx0aWiadO/DcxEfZ44UebO2WBJtNB3Wl8ABn46EH4HfgXsqDVDVgLx8NjHqhPfpGcTKleqTPj8cFb1hVUPANvBMsfCfa/fl+tGsBNTrphy3Wk/qD1lXGUIWxSmJuM4gT/AstDCw+89HNxGUKPRFDn8rlGklOellAOBUsB/gNbG31JSysellOd8lSWEuMboCSKEiAI6ALszpHEP/94VyPuI1gWERz5+hD7j+xC5NlKFWrPD9VWuZ9o/0yhWQsVNvOnWmxj5y0iuOXuNcms6DuX+LceIpSNo0LkBAGHhYYxYNoK77ruLsAthmE4Kap6ryTNfP0PbxwIfqi0yJpKxa8fSqVMnSvxUAnFWcKPzRob/NJyGdzUMeH5X0FMlNJoiScBmREspHcDObBNmTSww0xgnNAFzpZSLhRATgD+klD8CzwghuqL6LudRY5T5BpfTRcrlFKKKR/k9ATzxfCL2VDulry2daxl3DL+DTs91oqZtEpEpEfx3xQuZ0tRtU5fXdr1GHedkAF7fMypTGkukha6julLTtROXS/JS/CjU2su5x2FzKBnhmY8VK1mMXpN70WtyL2LiY3hhaGa98zNSSlJSEoiO9t/Xy+GwYbOlEBVVwmuZu1wuUlISiIyM0QvIajQ5JF+9MVLKv4BMn/xSyjFu2yMh/wXDdNgcfD/ue1Z+uBKH1YHZYqbdwHb0mNCD8AgPNX0WbF24lRmDZpB8OhkAc7SZrs93pduYbjmS43K5+Pihj9k4fyMrl0pq1qjBsKbDePaHZ6lSr8qVdPs37uer57+i6sSDAIwbOY77X7uf2rfUvpIm4UwC3zz/DdUe3QvAkAeG0O2/3Wj/RPscN4iLpyzmh1d+4JeFDgCe6PsEj7z7iMf4p3lCAHuBUkri42fwww9TSUz8l6lTJzBnzpfcf/8rlCxZIXsBbiQknGHBN0PZ+Md8kC5KRpehY7fRxLV/8kqZu1wulv88lRVLXiM5NRFhMtHy/x7gnnunERVVImDXpdEUZnLUZRFCrBRCrBBCVHb735ffiuCon3+Y3ns6vyz8hdT7U3G84MD6oJUVP6/gjbvfyFE0lP+t+B/Te00nuUEy/Bd4CZxdnCyYsoDvRn2XI52m3j6VDUs2IO+TKgaQBc6XP8/YZmO5cOICAAc2H+DVTq9ysNzBKxPqD1U8xGtdXrsSgcaaZGVCqwls3rcZWQnkdZDQKYE5r8zh+3Hf50inhRMWMm/8PBx3OJRO10FK4xTeu+89tv28LUey8iM//vgqs2dP59KlmTidCUhZnz/+KMf48W1JSbmcvQADqzWJaROaU2/zdxx0WEl02lmQcIotc0aw+PvRV9LNmTmIgz++zPKk8yQ6beyyp1Jx3SzemtQKhyPjOgoajcYTObXdxRm/Yhn+9+VXaDmw+QC71+1WXo7ljJ1lwX6PnQPbDmQKaZYVnz/5uQpM1wKwoO7Q9UAvWDJ9ic8hzc4fO8/uVbvhIeA65Qk6d+WT0AlcVVx8NfgrAOa8OAdbGxs0gHZfqh83g62djdn/nQ3A2llrSbAk4OzohLRY3ZXB1tvG0jeWknQhySedXC4XP772I3QnfQK/CWgGtIaZg2f6JCegBLA3mJKSwOLFr2Kz/YS6iQIIw+V6jcTEevz22xc+y1q3dhY3JpxiutNOeUNSc2C5LYnlS6eRlHSBc+eOsGHdLJbZkmlgnFcZ+MJhpeSZg/z5548BuzaNpjCTI9OolNKU1f9Flb9+/gv79fbMpWkGax0r237aRu2WtT2em5Gzh85m8JM1qAEuh4sDGw9Q6/+yn0y+5tM1KvR5KQ8HG8LOlTtxuVzsXbkXRmROsuoN4Mh+6romc0PLozzc2gbRGSbdA6Y+DmIdkymOmv/kaQmnNM9Qa4qVXxcbodxEBln3A8cvcL2bF2lBWw5q797fCQtrjN2eObSuzfYAmzZ9RMeOg32StWvTHJ63JWfaHws0N1vYvTuexMRzdBYmSmZII4D+1kTmbv6Opk175PxCNJoihm7IAoApzIRweR4nE1JgNudgySMBeOr0SfUzh/smyxxm9iwHJV+YlL7CJLznByCMuGverLvSSOIT/jnX5HdMJjNkWtY1DYdx3DeEKSwLSSovk8mMzUvhOwCTdprRaHzCrzdFCFFNSnkoQLoUWBp2bcjiNxYrE6P7lDobhO8Kp9Fbvi/NFFs7lpNbTqo1O9zZA+YIM9WbVPdJTtzjccwfNx/Okm6uBdWgbYaGHRpiMpm46fab2PHnDvi/q89vNxhucN7AiLYj+P3P35k1ZRbWvlZW9TeOzwTOg+XzcN4+/hJReI8sc6VnFw2PdXsMx20OqJdhAv8vcM25a3htVx71AoMwTaJOndY4nTuBPVwdXldisXxKy5bdfZZVv+UDfLR/Pfdak676fDgAbHXZ6XN9O6zWREa7nJwGyrulcQAfR8TQofm9flyNRlN08LdHeEAIsU8I8YkQoq8RaabIUaV+FZp0bYJlrgWOomqiY2D5zsLNt91MtUbVfJY14LMBKsDcClSsHiuwFVgAvcb18llO8XLFadqjKXyBmonpMH7fQ9i5MO6ffj8Afaf0JXJTJKwDkoEUYANErIvg3ldVRdq8b3PKx5Qn7Mcw1eGRwB41ob77hO5EFfc9vFrfiX1hIfAHqifqBFYBm2HAjAE+y8mPREQUo3fvSVgsdwA/om6elbCwhylX7iwtWz7gs6zmzftyunxN7guLYI8haTFwq6UYXbtPJCqqOKVKxdKh41DiLMX4FXCtgsRVcHd4FGFVG/Cf/3iysWs0moz42xDOA4qjAnl9DZwUQuwQQrwthOgqhCgy/tuPffoYPQb3oPTK0ogpgpK/lOSeQffwxKycLfBZo2kNRq0cRdl/y6rljF+BqA1R9H+vP7cPy1nF9tS3T9H56c6ELQmDScAZqBJRhal/TyWmjBrTq3RTJcasHUODmAaYp5sxv2Xm5sibGf3baK67WY11hUeE82L8i3To0AHTaRMcgdi/Yxnw7gBuH5oznTo83YFHP3mUYn8UgyPAMShzrAwjfxlJnVvq5EhWrgjypPkOHQbx+ONvUKnSKwgRg8m0h3btyjN69AosFt8/GMLDIxj64lqSOjzNLcVKES1MPB97A10GzKTD7cOupOvaczK3PPgej19Tg9+Av8xhRNzxPE8N/zVHpliNpijjV6zRK0KE+A9wq/FrjWocJeqbfyuwQkqZeZZ2HlLUYo1mxOVyUWJNiSx1T3sWspoXmOb4EghHFl9lBbTM8zhyTF7Gj+zYsS1CCJYti/dbVqD0djgcmEymPA2Llx9jdvpCftRbxxrNAVLKv6SUb0op7wTKoJZmGg+cQ8Ui9eCXqMkLNn23iRH1R/BI2CMc3naYLwd/SdLFq6c7HN1xlNfvep1HIx7lEcsjTO0ylcPbDodIY01OcLlcfPzhAzzzSDh79qxh7+7VTB7fksTE8yHVa9GiSQwfEMWAx8J5/BEz456vweHDBX+eqKZwEtDPNCFEXeBxYDhq+aW0McN/ApmPxjeWT1/OjMEzOHXzKRgNsqxk9cbVTGg1AWuyilt+dMdRJradyE7HTlzPuZDDJX/zN5PbTebwn4WsMSyEcURfmdCSsxtms8jloC3QBKh1cCNjnquOwxGaBYxnfzOMlfNf4n17KqnAYaDH2YO8Nq4xJ0/uCYlOGk1W+NUQCiEqCSEeEkLMFEIcA/4G3kW9jz+i4oBWkVJe77emmhxhTbIyb/Q8bL1tUBt1p8PA2dnJBXmBtV+uBeDbkd9ibWGsBh+J8nptBtZbrHwz4ptMcpcxMmDz+wIpqyhy6NBWjh7ayFpctEFNTikG/IiL8tZEFi58Oc91cjhsrP3lLRYDPVExIWKB14Ee0sWsTx/Nc500muzwd6LREePvOdSSSStR44G6Bxhi9vy2B1OsCTKuLyvAVs/G+nnriRsYx9/L/lb994zcDHun7sVhdxAWXsDnoxWynmCnTnEAnDy5m+5t3WbHqN2YV8GvuDgS/jrVa/525bxAjB1mx9atP1BSSlp6OPY4sOjAxqDroNHkFH9ruDSviiPAfmAfagKBJtRIvM9fF+mOMV7TFe6574WGLG+T/35wOcblkl7NTDp6hya/4m9DeCvQ3vj7HPACYBNCrEfNhFsBbJJS+hYgUxMwareqjeu4Cy4AGVZxsvxtofnA5phMJup2qMvu7buVS5M7O6Bm65oFuzdYyHqCaaT17Pbt28i0iS04jnGLV6njrnZwOyau6/wMvXu/mqe6NWrUja8QbEZmeqQ+BcpX9T24hEaTV/i7Qv0qKeVoKWVLlLdoN+BDlEFuArAWuCCEyPfRf72tll5Q84sqHkXXl7pi+c4Ch1QUl97t38e8zEyJ1BK07t8agL6T+2L53aIm8duN31awrLZw7xQdmSQ/U6tWc2Ir/Yc2mNhs7EsF+mDiWHgUd989PtM5nTrFXTGtBgOLJZJmbQfQGfgJFS/hPDAG+AbB/Y98miu5wdY71PlpQkvArBVSykQp5WIp5bOoAGHPAWdQcwq7BCofje/c+cKdPDjpQcr8Vka57tkEza9vzth1Y4mMiQSgWqNqjPx1JLWTaiNeFZimmqiVUIsRy0ZQs3nN0F6AJltGjd9CRIOutBMmVgObgK2V6jPutX1YLJEh0emhhz+ieZeRPGQOJwKoAHxRMpZnX1pLlSr1QqKTRpMVAbF7CSGKA21Jn1R/U9oh1AdhfCDy0eSc1v1b06pfK+q4phB+OYyBnw3MlKZ6k+q8uOJFHDYHUsocLyScLymkZtGMhIWFMXjoAlwuF7Vqt8ZkCmPMpNWhVotevSbTq9dkEhPPY7EUC1mjrNH4gr/TJ14WQqxDeY0uBIYA1YHlqEn0TYBrpJQ9/VU0WBzZfoQ3u7/Jnt/2snftP3w68FPOH8v5ZGQpJfGfxPPCTS8wIGYAw28YzsoPV2ZaP3DVR6t4ouIT7F69h92r9zC6yWj+/effQF1OJv5a+hcT2kzgn3X/kJpoZd6YeViTrB7ThlnCCkcjGCScTgdLlrzJsGE3MmBASUaNasH69d/kaOHlYGEymTCbw71GBdq0aS4jn6nE3t2r2bt7NWNG3MCBA5uvSpOUdIF5s4fxwlNlOXL4T6ZPbsOePWv80ismpozXRvDChRN88/lAnnuiFM8MjObDaZ05fPjPq9I4nQ6WLXmdg/vW88+eNUwZdaPHMv/rr6WMH38rAwaU4siRncybNw6r1bd1Mt2xWpP5ccFYDvyzjn/2rGHa2CZs2/ZTjuVoChb+mkZfBBoB61GRZNoApaWUd0gpX5dSbpX5oZbwwv6N+5nYdiLbU7Yjr5W4yrtYu3stY5qOyXFj+MVTX/DNpG843fg09qftnGl+hm+nfsunA9LHRH4Y/wMzh8wkpVmKWkH1WjjqOsqom0dx5vCZAF8drPlsDe8++C4HKxxEXishTLLsx2VMbDcRW2poJlsHgn4zr/5dRZBiiUopefvte1mw4CfOn5+B3X6IEydG8/nnk5g/f0LA8wska9Z8yhfv9+H5hBO0QPlF9Ti1h9cntLjSGKakJPDauCaUXfEeq5LOU1+6eHrvb3wy7Xa2bPkh4DpdvHiSV8c25MbfPmdjyiX22pLpu2MJb05qxd69vwOqzD95uxsnFozlRoeNFlIy7cQuVn0+gEXzX3K7vi94992BHDz4GHb7AVyumixbtouJEztis6X6rJPDYWP6lLY4fp5KfaedFlLy0uEtfPd+b+JXfhDwMtDkH/xtCG9HNXxtpZQTpJS/SykdgVAsL5g5ZCa29jY1mdwMhIPrVhfJtZL5YaLvL/+JXSdYN3sdtnttUBOIAqqDra+NzQs3c/SvozgcDha+uhD6AI3T86MbuGq4+OzRzwJ6bbYUG18/9zW2Xjaob+QnwN7NzulLp9k4u/DM58rUGAaBXbtWsWfPTrfV50sDXbDZVrJ06VtcvHgy+Epkw7Jl8R7nCv4wawgfAsNQE9yjgDeQDEAy8yP10RC/8gMaXjzJ5w4bN6LGTB4G5ttS+O6LgbhczsDqumgSvZIu8KbLQU3UOOIQ4ENbMt/PHASoMj+7ZzXLbMmURL0uXYA1tmR+WTqNixdPYrOl8PXXw7HZfgbuRfnsRWG3z+b06Ug2bpzts06bNn1H8ZO7WGBPpbiRX29ghS2Z7799Plc9TE3BwK8xQinl8kApklekeWo67U6qTT2CrELm1dLvc2E6/Tt1OHXlvKwioGxZsAXnjU4VmcWdCFj2i42yxd8i8kIEK5dJqKIOXZVfHxBndlE3AKuzp11fUkoSy5facVVwz+8Aqx4Gelsp5viG6/jd7/zyGm+NXtr+YLWJGzcuwGp9mKsXnASogMnUhW3bFhEXl3n8NVSkeTzabMksWJ5Eq7QDccbfVTAFSXd2Uef6OG6ovZnaT6Yg2l0tpw0QbU/hyJFtVKvWOGD6/blxDtOcmZce7r0KKom/qVazJQ1uPsRTdySpEnfTuwKwQdgxVYgjPDyS5ctttG2b0QnHhNU6kN9//4rWrR/2qoe7Z2j9G/+i0r1Jqnfgll9dIN5kpfi1LYiJUREq8iI4gSbvKLJzXKWUypXH22TyHFh0HTYH0uwlvTF53eWSWU9w9zk337hyfVnoVBjpx0z6BaE5dDjsZP7SUUgZZRzPf7hc0utj7v7yS7xPhI/CFPDrc7gcXkoTBAIpXSBdWUzOl0jpUumy0Dwnesss8yu874wmQF6jBYm0no8Mlwx/bDhnm52FWlevli7WCRqUbMCQVkN8klm/U32WfrgUa5xVmSDTcMLtXSIYNvtxqjapyqBbB6mVGytkWJ19CVRxVOHlP/zvlaVdX5I5iaG3DsU+0AUlVH41o2rQbuYBLAst3HN/N+5ocIff+eUVOTV/9mMmMwncWGGDBrfxxx/TsFoHc3Wzkgz8SL16nuLUhY60HovL5WLoY5HMddnpBFcm3dMO3kbwVslKTHk7nvnfDqfSL9P5gKvHjncDx5BUrdowoPrdVK8jX2+ex0sZYm2sbgf9Sl/L+DfW88cf37NxRn82WRMRbnonA63CIxn+8k8UL16eoUOrASeAa6+SZbHMpkmTjlnq4d6z+/WXd0j47r8stCVfVU6ngdbh4Ux5c/WVHqGmcFFke4RCCO599V4sP1vU2hjS+P0Jlk0Weozp4bOsmi1qUqNBDVJHRKMAACAASURBVMIXhsNFY+clCF8UznV1rqNO6zpEFoukWc9m8BVqTh+o1RrXqzwf/sC7+SY3RJeKpuPQjljmWcBt+MoUbyLqbBRtHm0T0PyCRb+ZUPZcLs8NYO+wYcOulCvnJCxsEFwxmf+DxXIPDRrcQcWKebCocC4wmUy0vv05+gJLSV8k9HNgHJKeD00H4NbbhzEvIpqpwkTaSNh64E5LMe68Zzzh4RlNwv7R6e7xvGEpxseA1dDrF+BeSzHuuvcNhBA0bNiVhHJVGRBmwdUAaKBe1bssUdRrcBcVK9YhOroUHTsOxWK5C0hb5smJyTSGqKhNtGnziM863dKqP1ujy/BfUxhp/ci/gDssxWjX/kndCBZiimxDCND47sYM+mwQ12y5BnEMxFGodrYaI5aPoHK9yj7LEULw7IJnaR3XGstnFsLfCscyw0KrW1rx/E/PX3Fpf/LrJ2nduzVitlCN4VGI2hbFsB+GUaNpjYBfX88JPek+tDvRP0QjjgmwQf3S9Rm7bizRpaIDnl9+JRANYlhYOKNGLaNZM0lYWB3CwysSGXkLHTo05/HHPwmQpsGhV+8ptOoyir4mC78BvwPDLcW579EvaNz4HgBKlYpl+NhNzLs+jvJmC9uEoGfJisTd/zYdOj0bcJ2uvfZ6ho5aw/s1mlPGHE7psAgGlq1GzwFf0rRZL0CV+ZBRv3OwWW8SgEtAs8jilOjwDA8+/tUVWT17jqV794eIjr6L8PAKwF/Ur7+HsWNXEx1dymedoqKK8/zYTfx+cxfWC8FaYaJDdGnqdhvDPX1fD2wBaPIVAVmhviCQ1Qr1Uko62CchhODX8BezlJPdaul2q53Ec4nElI3xOifP4XBwa+pEzOFmVkaM9vkacovL6eI2xyQikiP4ufQLQc8vELibQuNjYohLDMwK9YEwl9psKSQnXyQmphxhYVnPu8xPq467XC7at2+OyWRm5coNXtOlpCRQpswmkpLa58nK8klJF3E4rJQoUd7rPMi+fUsBkq++Ouu1zF0uJwkJp6lUaRdJSe390um221rjcjlZvnwNZnPejCDlp2cljaKyQn2RGyP0hBCCMIs5+4RZIKXkj/l/sPiNxZw9eJayVcvSeWhnmvdpnunlDgsLIzLGu6kp8XwiP7/2M+vmrMNpdXLTbTfRbWQ3YuvGXkljs9mY2n4q+7buAyeIMEHzu5sz6OtBmeSZzCbCzGGQ4tclFgrSeob+NIgWSxQWS1SgVMoTTp3ax/KFE6hbaztCCOZ/O5yOXUZQvHi5TGmjokpgNof51QguWTKNJd+PxWlPwoSJYuWqMvS5pcTGZjYhZ9Vrs9lsTJ3anru7XUICwwdEUbd5HwYN+jpTWpPJTKlSsQjh/+K/JpMZk8mcZ42gJrQUadNoIJkzcg4zhszgcJXDJPVN4ki1I3w2/DO+Hpb5hc2KxHOJjGk6huUrlnPxtotc7n6ZTUc3Me7/xnFoyyEAnE4nT1d4mn379kF34AmQt0s2/LiBETeNCPzFaQo0R45s55WxjWi5/hsaOO3Uc9io8Ot0poz+f/bOO66q8o/j73P3vYCggLgFUcy9NTe4NS1XNqy0oWlZmnun5irNylVamla/TCstNbeCM0furThwDxBk3/n8/jiAF7gocEG07vv14qWc85zn+Z7nXs73POPz/dYgNvZOnrf3ww8DWLN8KGPNiRwBNmOjQWQEk0dV4vbtC9mux2q1MmJAEQzhuzEAhYC5wsrlvT8zboQr17eLvCNHrzuSJI3PZTtCCPH402XnAGd0dHcu3GHr11sxv2uG1KU3LzD5m9i+YDst3m1BiWfS72jLqr0109dw3+c+1g4PBMy2ZjaMHka+H/A9E/+eyK8jfsVkMcF7yAppkPXdZeH2nNuc232OoMbp37w3Mgr3I+7wkGndJ4XHIZDPi5Hh08LvS95lanIc/QBSdILfYqJ/3F3W//kJL70+J8/aslqt7N/2Nb8AnVJEQeWBP7DxHAq++qITU6efylZdv/46Ap0pgb8Bdco+mFeBpkDQ7bOcO7eboKDGeWa7PS6d4H+LnI77J+SyHQE80Y7QGQ78dgBbZdsDJ5iKHqxVrOxfsZ/O4ztnq649y/Zgfd5BFI/qcP2L69y/fZ/tP2yH+jxwgql4AoGwYvgKxu4em7mOJ5zH4QAztZnHMosnjfj4e4RHHMbRnuSBVjPN9/6cp44wNPRrdNjomOG4BAzDRudb5x56vb3AvXzAbkqNAAOkE7iXBg4ACfoOabIOl+Ny4Qw5dYQhjy7y38OUaMKqcRyCyqa2YUrMflxPi9GSOXgJgAIUGgWmJBM2qy0rbTfoyVF7Lv7do0OLxYhGocDR19MTMFocB2DPLQkJ0bjLkniH7eUsdIQtyweUCvI87JuL/y45coRCiILP7/IEUqlFJTZ8lyKot191tYH2gpbKoytnu65ngp/hyOkjiIYZHhjXQOemw7uMN+Xrlufk0ZNy9GT7fTgW4Aw0mdaEp4mCGAk64t/oED09i+Hu7s2O6Os0z3DuN6BSUNM8bS84uB/rV43nLHJoMnuWoUChL/zQ6+1HdjNmtMXt5Cb2QjpBvRFoCHR4dTJt2gzKM9td/HdxbZbJAyo2q0jJciVRrVORpkZOBNUGFX7F/ajcMvuOsOu4rqj3quEksvIZ4Dpo1mh4cdKLKBQK+i/rL6c83oSckhwgDlgOSpWSNoMeHk3jSaEt09Jiozpf2TT5Jw/IjxBtWZHfmdAlSeK5Hp/RU2MgVTBhA1YBH2sMtOs2Odd1O7Ld09MXH7/KtEfiBEAo2EJhETAPGy+lCPgfVQ9A//7LOAUMRX7HAzk2RDcApSbXTvDfnn3+335/+YHLEeYBkiQxbN0w6laui3q+Gt1CHap5KmpXqM3ITSNztA29dPXSDF07lOLniqOerUY7X4v7X+70nNaTpr3lt3f3Iu6M2jwK5QklzAQ+B74C/X09n5//PH9uMp8oln+pGJ0iv2KWFgTPNnyV53p9TddCRSmldaOYRs9wvwq8O3hdngbSTmXi1GNYS1SnPrAH2A18hIrnuk+lYcNXs12Pu3sRPhi1ne+VWvYgR7opB+zXezHh84hHXO3CRfZxWiQjSVJxYCzQFihJ5i0cIO8a/VcLcvQeevot7Ufi7ESir0fjVcIr19FbgpoEMe3INCIjIjEnm/Er74dCmd6ZVmxWkUWxi7iw/wIRByOo0qYKfoF+eXErLuz4t0yXNmr8Bs82fI07d8JRKjX4+JTNUrzuLEqlkklTj5CQcJ/CfvVQq7XMWXI8V3VVrNiMLxYlU7psHZKT45j46Xr8/ALz2GIX/3WczVBfEvgHeBd5UlALXEEOCWhFXsE6Cux0zkznuXn2JvtX7M8UQT76ejQ/D/2ZYZWHMbLGSNZMW0NSbO6V5wZPAyUrl3QqhFn0jWg+f+5zRtQYwZi6Yxhffzzh+8LTlRFC8M/Kf1g2chl/ffEX/xv6P87tyrwj7+qxq8x/fT5DKg7hxpkb7Px+J1ZLwW4yiL4ezZG2v3F5dwT7L15h6pp1xCblrs+nrV1LkfdGs/3MBXadu8TkP//MVCb89m3eWfw/AoZOotqYz5izeQvJppxtKOrFUmw2G8uXD2dYv8IMekvDyA9KsHXr/Exlr149xpL5LzNpiD+3b5xm587vsVrTp+m8efMs06c0J/zsDsLP7GD65KZcv346ZzefQxQKBcWKBeHr6+/QCd6/f5cJH9dhcG8FVy8fZGi/QoSFZQ4fd+LEJj79tDPh4Qe4cuUEx45tyFTm5MmtfDapIVG3w7l9/RTz5nQnISEmXZmkpDjW//UZVy7uJ+LCPlb89CFRUVcd2q7Xe1C4cAmnnGBcXCR//j6OiJT2/vhtDLGx6RNi22xWdu/+gVkT6jJpiD/fz+tBRMThXLeZV5w4sYn5n7Zi4uCyzJvewmGf374dzv8Wv8PlC3u5cukAWzbPyVFi4v8yzk6NjgeKAe2EEDVSjn0vhHgGeRZjI3Ie0K7ZrVCSJJ0kSfslSToqSdJJSZImOiijlSRpuSRJ4ZIk7ZMkyf9R9RrVRhYNXcSidxelOcObZ28yptYYtu7dyt3Gd7lV6xarV6xmfIPxJEQXTBLOqCtRDA0ayvELx7F2tmJ7xcY1zTUmN53MoT8PpZX74YMf+Hbgt4QXCSeqZRTHzMeY2Xkm2xZsSytzdN1RPmn+CQduHyAqJAqT1sSPU37ky65fyjtPC4Bnp99kYtBk5m0tR7K5MommID5ZbaPW+E+JTshZn7f+7CtG/7aT6MRPENTEYgtg3Kp9NJvyYHp434UL1Bo/nSW76nM5cgUnrn/ByF9v02TqVzl2hp+MrcGZ9TOZnRxDmM3M2LibrP1xAAu/fi2tzNGj65j1SUPaHPiV36MiKGlK5OSPH7Dwy05puxwjIo4weXQV6pzfTTUhqIagfvgepo6tSnh4wSRMvn//LuMGFaNMxCH+hyAI6J8cx4olfVm2bHBaudWrP2P27H6cPv08FktlEhN9mDfvA1aufKCO2rFjEfNmtOKV22eogaCSsGE4uIqxH5VOc4aJifeZMaEOxj8mUMGURCVzMhVDv2Hq2Gpcv34yz+8vJuYmU8dWw3PDDJ4xJfGMORnvDZ8zdWzVNOdrs9n4bnYXDv/wHhMvH+T3qAie++d3vprShEOHMr9gPS7Wr57C8tldePf0Vv64d4X3z4Sycl531qx8IOu+cGEf08fXov6uJVQxGylvTOT2ryP5amoTlzPMBs46wrbABiHElownhBDXgBeRHWEmZ/YQjECLFMdaE2gnSdKzGcq8DUQLIcoDXwCfPrJWHRhfM3Jg9QHC98ijqyUDlpBUJwlrayuUAgLA3NlMtFs0q6euzoHJecf8nvOx+lvhZdkeSgLtgNaw4O0FAFw8cJHdy3djfM0oZ58vBtQD06smlg1dRvy9eKwWKwvfXIipiwnRWEBxQAemV0ycO36Of1b+UyD313fJb9xPGoPZ+hXgAXiRbP6Va9HNmLx6fbbr+Ts8nC2nzgKHgLeQRZzFgEPsPH+JbadPI4Tg9YU/E2/8GqttMlAHaEOiaQOnbpRkQWj2N0Ev3bmT2BsnOIzgZaAG8D6wH8HBff/j9u0LWK0Wflr4Gn+aEhklbNRElgxsNyVgPLeTf/5ZCcB3c7rTR9j4CSueKWWWYmOAECye91K2bcpLvviiPdWFjVDkr5sBmIScLGXPxi+wWq1ERV1hzZpPMZl2Yd/nRuMu1q+fzZ07F7DZbPy+9H0WIyf+9UDOGb8NGzVMiSxeJGeD2PjXpzSIusJKcxKFkaPGzLKamZgUy4rF72Syb+PGMKe0gquXD+PVuEi+NxvT+vw7i5E346L4M8XRHz26lvunt7HLmEBn5IfPMGHjL1MiP33bC4vl8cuSoqKusGHNZPaaEnkLqA70AvYaE9i2fgZ37lxACMHPC1/na2M8k23WtD7fYEqk5I1TbA9d8Njtftpwdt2uGLDC7ncrsuMDQAgRL0nSZuAF4MPsVCjk4Vpq+BN1yk9G8dELPBD3/wbMlSRJEo+KIK4FUzUTO3/cSYkqJQjfHY74KPMllvoWdv+8m1dmvJIdk/OUi/9clL/pGWeuaoNxk5Hrp6+z+6fdmKuZM2sJvUERqODw6sP4lPXB6maFshnKqMBY08j2pdup/2L9/LsRO1J3hlotNvwXXEAIA/AnwcGyIwoNDQYSUCuPQoXzDy7c6CD6TsrOUPcq1whtWQg51gh2db0EFKKwYQ5Jxfz4bvk1bOJrQkLsHYyCJNNQFm7/kIFtW2frHmZv2Mz7SHhm+CqWB4JRsnbtNBo16kkJq4WMggQNsH5DAjfc3qV4qXn4/3qBhqkng1P+DYVPELQngqCKzdOmLh+XUDz28iFGkvnNuDOwIRS8ilYEBHXqaBEic5/XrBmLWl0VhUJJ1y5GCoGcFSlYrkcRCptq2khkFXqDF507x+ImBJJdGUKhP4Lq0j4CAhunBdfObR/Y75wM8N/Bs6mPB7v2JmLlb+l3KgQ1p3qVE4zonIA2g1r6WSBQCE6fDqVatba5siW37N+/gh7CliHTIvgBezcbMXu3wqOQL0HLL5CmuAqW/1GEwjKSCNeMpky5VWnXuoIPZMap7BOSJN0BfhZCDLL7fYMQ4g27MjOB94T89MtuvUrgIPJzZp4QYkSG8yeQp2Ovpfx+AWgghIjMUK4v0BfA09uzzrg54yARDAoD3qW8uXbyGsLPwf1bQYqUKFszoxcBZbwSq3v+rbFdPnQZipI+wW8qt6BYhWLERcaRYEnIHMkGkO5LFC5SGJVWReT1SGyFH0yBltKW4prxGiSD1qRNF8Q7P2lWU57CUZogwWgE3AEoXfooAFev1kB+10nAQ/cgmsCRHZMAiFcqcbfKfV6zmVxXosmE1aYmdW9W+rpMKBVmtCo1SSYLAgMrVnyZwaokNKpwqpXK+IhxzMlr1ylqMePr4NxlIMlQGHd3b5IiLxFkJ/SOL1UK92vXqNpjPkaFEo3GQHJyHB4p571KXwQg5qqchisO0Go9SF3C27Ejo935w9XLBwkiJYqLnd0AFXrMR6HSABIWCzjqc1/f80iSQJIkhM2GEoi/WyLd/bn53sAGSAolwmZFASRkKAMQj4RG65b2MpDTPlAq47Fa3WnW7IG84lF9rtN5YDIlorVZObHivUx1hktKtL5lMRgeroN0hlS77YmJuYE+5mYmRwhQqcd8bCoNCoUaiykRt5SXNPv7swJJkgKt9sHDIif92b17yH8i+4SzjvAAcEUI0S3l981AJSBICJEoSZIC+b1QJ4TIceZSSZK8kCVPHwghTtgdPwm0zeAI6wshskzhKpWQBO+C9nctrwx4hWbvNGOQ/yBi28TKMZvsOQiVTJUYsSFzAOtHpWFylgGlBhBfPR4aZDhxA1gK38V+x95le/nxsx8x9sgQFcQKmq81jN44Gq8SXgytMBTL+5a0MfrMoJkMPTcU9Xo17dq2o9vE7Ccfzgte/95GiUFjuR37O/BsykgQQkLCgG8JqbSQbSMGZLrOURqm6WvXMuq3I8A5QLKrKxSowscvBDG8QweKfjicBONh5HnmBygVE3it4S6W9Hk9W7b3WbyY0zt2s4v0L0EWoBgS3fr+SOXKLRk31J8rFiOp+RTCZs4keOhQ+qh1RLUbygvdPmHwO258Z0mkC6TLhP4X8JpSy5eLHv+azrD+XryVdJ/pGey+CgQBH087S1RUBHPnDiM5+TAZ+1ynq0+/fhMIDGzAsA98OUVKj9vd33gULPEtzyczzvLN5+3peXwDAzKUOQS0d/dm+uzbKBS5ywjjKJ3Rp2OrM+XacYd9/lGxioyZfoa/Vk9Bt2YyP5jT938cUEqlZcJn4RQpkv08pXlh94kTm1k3tysnkuPTTRIJoJbOg5B+y6hUKYThHxblsDEhU59PUCjZ1fA1Xu+zJFc2/VfSMDm7RrgVCJEkKTVB2FKgBLBHkqQZyBKiKsDy3FQuhIgBwpCXLey5Ror7kiRJhTzlf+9R9Sl2K9DF6GjYsyEKhYKu47ui+UsDqQH4BXARNDs1dB2f7f09ecpLU16Se/UCDyaE7wIroF7neqg0Kur3qI8h0YBip+KB0jgJVOtUBNQMwL+2P17FvHj25WfR/Kl5MNEsgEOguqCiZf+Wj+2eei2VfxQKBZ90bY9B0xOwD7y8Db1mFJO7Zn/aaWi7dmhVUcAAHtygDfgItfImozt1wqDVMrhtawya7siZkEHuhJXoVF8xqmOrbLf3aY8eHJMEE1GQ+voRA7yGAq3ejUaNeuLlVYwGz75Cd41BzmEfCjV7zOc7YKVKS/OW7wPQrONw3gbst8UcBHoh0ciJJLjOCKm7vD6XOcjrHKlzCFeR1yCKeBanePEgKlduiY+PHqVyEBBPzZpHqFnzCErlMLy8bFSv3g4PDx8Cg4LphCJDj8NMbLz4ujwaadNlEh9rDGzmwdf8DHKG+vadJ+baCWZFuxen01+jz9TnfTQG2r0ou/9mIe+yXq1nPlLan9UdoIdaT9263fLVCWZF5cotsfoE8IFSk/YtTwSGKNXEe5WgevV2aLUGWrcdTHeNIVOff6XS0apj7hMK/Fdw1hEuQt6o4gMghPgJ+AqoCgxBHtcsB6Zkt0JJknxTRoJIkqQHWiH/jdizGtKEXd2BbY9aH5TuSAQYAxi7YyxaN3n6LfidYLqP7o5+uR7dYh3ahVo8wzx578f3qNCoQnZNzlOa9mpKl3FdkH6TYBYwG1gINZrXoP/P/QHQ6DWM3TGWctZyqGer0f+oRz1PTc2gmgxa+WA6qPf83jRp0wT1N2r0P+iR7kqUvFaS0dtG41Us+5m7c0uqA7SnT3BTpnZvQiF9U5SKAygVeynu+QYr3nudRhWy3+cqlYrjk0fg4/EH8orJAWA3RdxWcHTSMDQqefl7QucODGlXDoOmOoX01XHTlCbAZygbh71PxeLZnxou4u7O5jGjWaDT4QM8g4LiwKHCRTgw9ZM0Af7LvReibdKbCmod/yiUxCMxs2RVPhq9Ey+vYgB07vwxtUPeoyUK/kYWijdHolqzd+jRI48i7eSQRo1eI7jDSPog9+ZJ5JHgbc/iTJhxGZBfZEaOXEuVKjdQq8sgv4Dc55lnLjB69IY05/XR8M0Q1IxnkJ39HuBNhZoX35hP9ertAShXrh69B/zOW14l2adQsF+hpKHOg3pdJhHcMvPUpLPUqNGBLr2/5Xl3b/YplOxTKGnvVoROb8ynTh05IL6Hhw+Dx+5mbqlqlNAYqKn3pLxaB41f59V3vs9zm7KDQqHgg5GhHKzSkpJqHbX0npRQ69j9TDADR+9M6/MOnSdQrt0QqmsMHFAo2SspGOoTwPvDNlK8eMZgdy4yki8Z6iVJ8kWWT1wWQtzO4bXVkUeWSmRHvUIIMUmSpEnAP0KI1ZIk6YAfgVrII8GXhRAXH1Zv6eqlxeRjjsNJmY1mrh67ikqjolS1Ug+NBJPfU6OpWCwWDv9xmOS4ZGp3qZ2lLjEyIpKYmzEUDSxKId9CDssk3k/kxukbVLlfBWPbvA2y/DAeFkPUaDZjbDkZhSRhCB3/0D5/VIb6IxERFO0xHw+dDo/tjjcoJxiNnLh2DXedjsolSjglJt997hynb9ygcYUKVCpZ0mGZrxM7065dCG5uFtavP+awjMmUTLNmcvaEHTsOo9FkFUk9e6SOBp3ZDGG1Wtm+fSFNm3py716DLHV7MTE36ds3CIVC4pdfYh2WiY29Q+vWTVEq1ezadczhZ2yz2WjRogE2m40tW3Y73Qfw8EzvVquFFi2eBQTbtu3LMvHu7dvhxMVFUrx4Rdzc8m9d0J5HZaiPiblJZORlvL3LULiw4++d0ZhA69ZNUSiUhIbudzpown9lajRfor0IIe4iT+jl5tpjyA4u4/Hxdv9PRpZmZBuVxvGtxkXGse2bbRz86yAqjYomLzehSe8maPSOAuQ8PlQqFfW613tkOZ+yPviUzZxl3B6Dp4Hyz5ZHHabGyONxhI8KpK1Vq9HqUx56TmRC3xsezqyNOxnWOg69JpnY8+czjSyvR0fz1aZQNp64SCGdlr7BtXm5QQPUqtx9/RsHBdE4KOsl7/Dbtzm+8WVq1rxImTJ+/PPPSmrX7pzOESQlxbF9+yKqVZNjzG3dOp/mzd/GYPBMKyOE4PDh1WzZspTY2EjKl69Fu3bvU6xY+ravXj1O2IaZVA46gFKl4/jxjZl2N8bFRbJt20IOHtyMSqWmSZOuNGnSC41Gn66cUqmkRYv+uLuHoVY7doLh4XvZuPEbevdOQqFQcP78HipUaJSpXKFCRfHykkfdWb3oKBQPNnLkhRN8FEqlCp3OPe3/WeHnVx4/v/JZnk/t81sRh/AsUoaGrT/M9x2lXl7F0/ozK7RaN/R6+YU4vyIH/RvJM0coSVJpZAfmCdwHDgshHIeJeEK4e/kukxpNIrlUMuaKZjDDtTnX2PrdVsaGjUXvoX90JS4KjLmbtzHi1w0kmYfznvE+CcY4npuxiEldWjKkvSyLOHb1Kk2nfkGy+WVMlg+BKA5fmcWinQfZNPS9tCnUvCLs9Gk6frEAk7UPPYw3sdnMfPvtJKpXX03//otRKBTExUUycWIwsbHP8O67pQCJVav2sXnzQj7+eDuenn4IIfj223c5eHAvRuNgwJ8bN7awZ09jBg5cRpUq8vrmgf2/suy7Xgy0mKhgtJJoTOT3Od04F9KXbq/MAuDu3ctMmtSc5OSWmM2jgESuXVvA1q1LGDt2M3q9R5b3k5HNm7/m118nYzYPQQgdVquVGTNeokuXobRvPzBP+/JJxb7Pm9usnL12gmlntqfrcxdPF05PjUqSVAGYD7RwcHob8L4Q4uHZOB8DAXUDxIR/JqQ7NqPjDE4ln0I0tesDAao/VbTr2I7un3TPVM/jmhrNDx6H7fmRUsnR1OiN6GgCh39MsvkI6XeEXkGnrs7Z6eMp4+1N1THTOXl9GLIAPBULBk1LPn85gH4t8i7FptVmo8TA0dyJWwK0JTQ0mMDAGMqU+Rs3bR3e6TedWrWeZ/HiAezeDVbr3HTXK5WDadAgnr59F3L8+Ebmzv0Io/EA6XUyobi5vcHs2ZewWIwM+6AoYaZEeQolZbfgvRCoqjHQd9R2AgLqMmNGV06dqosQo+3qEahUr9KuXRDdu2eeTnY0TRcdfYPhw6tiNh8EAux2jf6AWl2L6dMP4+1dxpkuzBMeNcXoDEZjQvo+T+Ee6fs8N+Sn3bnFNTWaDSRJKo+8Fu6NvM9xF3ALWWjfBGgJ7JIkqZEQIjzLigqApNgkzmw7gxiU4UVAAksDCzuW7HDoCF08GSzfvx9BVzLKIqAMNvESP/+9l+716nLx7j3IFDBbRaJpJF9vG5Y3jjBF5B+XmMRva2Ox2qYB0+wE5+0BK0XcXqdo6Vr4++9CiHqEZGjaah3B/v2BvPPON4SG/ojR+AGZwNxoKgAAIABJREFUxaIhrFt3n5Il62C1mtmx2UiN1G2ewfI/RUJhD4nEej2PT9FA/P33IMRdQkLsHaGExTKCHTu6OXSE6W4vZe3x3r1r1KqlRQg53/2D+3sDSdLh4xOczhH+G4XbR46spb5CmWntpgjQz5zM3zsW5doRuig4nJ0XmobsBAciC9/T1NspGsIPkEOgTQV6ONlWnpIcl4xCq8CqdSCOLwTJsa74fDnhcSfXvRefgNHseA3HZClDVPx1ohMSUCuLkuQwOkFJ7iXk7ejYYrPiOPkKgBaz1YqfuMUZkVU5X6xWExaLibi4aOT4eo7rslotWK0WtFnM6GgBq8WMzWZB3nfmuA+Sk6Mfek/2WK0WhNA6PCeEJlNg8X8jCQnRlLI5vs/SwsbW+znaG+jiCcFZR9gSWCeEmJPxRIpT/EqSpDbIEognCs9inmh1Wsw3zXIcTnvOQ5k6BT/F87RQEBnmnw0sh4duDXHJE8kYj85Dt5pGFRpQqUQJzNYrwHUyOhWF9BdNKmQcTeaSlFBwyffu0Xr4BIyWNYBH2tRoSEgYGtUABrYOoM5LXek3cgpnb2UO1gDb8PGphEajo1KlBly6tB6L5fkMZRJp1UowdepfJCTcY+HUplwxJcpuzk5I/b7WDbfuH9OiZX8+/DCA+PgvHLS3njJlHh1mL3Vkd+TIX3zzzUSSk0NJL6gPQ6drQp8+Q6hTp8sj63uaKVeuPguRsJL51WKN1o0ylRytELl40nFWR6hBjhzzMI4gxwt9olAoFXQa2QnNOo2sjE7lBmh2aOg6tmAE9S6yR7vq1fErdB+lYhyQGgzZjFIxCW/3W3SqWRN3nY7+IcEYNC8DdwkNDU55eG9Dp57BqE4Oggo4kem+VJEiPF+rFjr168j7xVL5DY3yZz5o1RyAKd3aYND0A07Z2XQOjaY/XbuOBKBFi76oVKuAX3ggOY9DrX6bqlXb4Ovrj79/bYqWrUU/lYbElBIC+BqJXSodjZu8gUKhpFOnYWg0bwJX7No7iEYziq5dh2fugrbB6cKTpVK9ejsKFTKhUHzMgz4XKBSTcXePpmbNTrnqt6cJR31uJX2fu3j6cNYRHkWOB/owygOOhVQFTJsP29C+T3s0izTof9ajW6pDv1JP7zm9qdyickGb5+IhKBUKdoweSL2Ateg1pfDUB6PXlKJ22d/YOXoQKqX8vv7ZS515s6knOnU5lIrDKBX78fV4lZUfvEX10hlj6znPD3160rl2LFpVaVSKI0ACZYoMYcvwAZT29gagW716zHipJe7axigVB1EoDqPVNqJ790E0bCgHevfyKsbIkevw8ZmMVlsRvb41KlUZatTQ0K/fd2nt9Rm0lhOVWlBSreNIipD6i+IV+WjMrrRt9G3aDKB9+x5oNDVRKI6gUBxEr+9I794zqVw5+yMYhULJ6NHrCQj4G42mLArFMSRpL2XLbmP06I0PlSP8m7Dv82C9J2U1hkx97uLpwtlYo88hxwJ9QQiRKYeO3fnOQoh1uW4oD3C0azSVpLgkLu67iEqjIrBhICp11n/Qrl2jD3hcU6KPEtSfu3WLi3fuEODrm2W0mKj4eKR201EqFLiHTUCZlXYxdTToKPNFDrgZE4O240yEuwdF1g93qOlKMplIbvkJCNBtG8cKTZ9MZYQQREQcIi4uklKlqlG4sOMg4ZGREXTu3AmVSpulkDopKY7WrZsgSQpCQ/enZXfISNu2wSiVMaxbl/Vkz61b5+jWrTsajY7Q0P1ZlisIHtfuy8jICG7ePIOXV3FKlarmtG7PtWu04HD2Fc4bWA+slSRpK7ADuI0cpak5sqRiDeAjSVK6OQMhxA9Otp1n6D30VGlVpaDNcJELTl2/zoLNm7lw/ToBxYvTt3VrqmUY6V24fZv3f/yZsdXuoJAk/lq1iokvvIDKTkNosVpZefAgVSpfwyZshG7cSO9mzSikz52WtLiXF7i5EaOQHD4gYxISGPzLL7z1zG0Q8N3Spcx6xUQRd3eWpuxyFUJw9uwO/t42n8SYm5Ss0JjmrQZkiipy9+4ltm+eTXCTCJRqHRcv7icwMH3U9ujoGyz730BqVD4JksQvvwyhe/fp6HQPksLYbDaOHVtPlSqnKV7ci9WrpxMS8g4eHpkDNhQrFoS7e5Fc9U0qVquFgwdXsnPnb5jNRmrVakmzZr2fmlGVj09ZfHwyZ6hJJSkpjt27lnLu4EqUag01Gvembt1uWb6AOEt8/D12hC3k0vH1aPRe1G3+NjVqdHxo1CYXMs6OCG3IyxKPehWyb0RCTjuYt1F1H8HDRoQ5wTUifPybY7IaEf60ezeDly6lv8VCXZuNwwoF81Qqpr36Km8FBwOw8fhx2n/+DYKOhIaeAMyEhAj8CiVyZdZENCoVRrOZF2bM4H5EBKs2GFEC77XVcFivJ+zjjylVJJcP/LbTiFEq8VqXfh3uSlQUFUZMxmSpTGioHCs+JMQXtfIYZ6ePIcDXlyXiDX776UNO7PqeQcZEAhBsVGn5RanmvaEb0yK5HDu2gcVzu/Gm1czoLWYSgQZtDTR7bgQdXpCDMUVEHGHmhLo0EVaWhMpBtZ8LgStadyZ8HoG7exFsNhvz5vXixImjbNiQTGCghsDA+qjVGxkzZjMlS2ZeKnAmpJvZbGTGjBeIiLiP0fgu4I5Gsxy9/jAffxzmVIDrJ2FkFRNzk5kT61Mr4R6vmxJJBOZr3UgqWZUPRoZmiugDztl969Y5Pv+kEa1NiXQzJxEFfKl1w6tSC975cFWug5j/V0aEzr4qvImsVH7zET9v2f2k/u7iKaQgdog64k5sLB8sWcJ2k4mJNhudgPE2G7tMJob+73/ciJZlAV1mL0UwDXnTiQ/yFuHj3I715b2l8s3M2bQJxeXL7DQaKQb4Ar+aTLweF8fAxYtzb+TGUWk5Fe3pMOtrTJYOwE7k5JNFgTDM1i60m/k1AGXPjOLMrm85YkxgEIIXgPkWIz8Y41k8pys2mxWTKYnF83uw1pTILKsZnxAoEwKHTYmE/jWdq1flpfmFn7elv7CyLqWlYsB+oI4xngXzZK3s3r3LOHHiDEbjPuQEMirM5sUkJn7M/PlvO749J7LGb9o0h8uXFRiNO4HeQHdMpl+Ji3udxYuf/gg1K5a8S8/7t1htSuRFZCXrHmMCpa8eZdP6mXne3o9fv8K4xHv8z5xEV6APcMiYgPn0Nnbv/jHP2/u34dTUqBDiCXksuvivsXzfPp5HTn5pTwXg780WrL5TidHpWbcpGvgN+N1OAN6WmjXPAUlg+Ie+nZMYtBBUGbKlj8HG39IJzOWnoFamvDPmdu0wZe1RCJhb9gLyqkKInU0hgBG4hKg4leJVbxLWzohXBtF9R8DblMjZszuJj4+kBtA4Q1PFgH0bk4gr3J4i3mUJ+PMOaZFAU+5PGQpra0ICoegNXnTpksCnn2o4cqS9nU3BgECSDhEQUB+NRp5GzQuh/LZtSzCZFpDxEWSzDebMmVIkJEQ/tmDXeU1SUhxHT2zkrwx6QyUwwZxM19Bv6PjCuDxr7+7dS9y6cZp+GWb3tMBYYwJjN8+madPeedbev5H/xjYvF07zpIwEU7kbG0uA2ezwnE4IzBYLyWYTssLH0cy9gtQZeyEcT42oALUkYbFaHzhCJxGIlHYdBZjWAgKrTWCyWByWAPCXJOLi7hIfH0W5LETseiDGbMJiMaLA8R/6gx4AWfbr6B4lJEmL1eq4r3NLQkIkmaMCAXigVBZ+qh1hcnIsBoUSR4nOAoDYxBgHZ3JPXFwkxVVq1OYkx+3F5Sr/wX+KPHGEKWmXuiG/oLsJId6xOx4AHBdCZP6UXLjIJbX8/Zml0zEhOXMEoDfbaXn3rZdpWL48IUPGIKfE9EsnAIdZFPf8khtffcJLkyfTMzyc1yGdKP0c8IJOy9W5o8HZ4NwpI0kF0Pqtj7DYxgEtM9i0A6X0PJbvx7Bi+XLiN2/mS9I7OiPwt8VMkzI1iY+P4keFAkcubFBbA4quIwlp8T6D+urZj5wh2/7+VgADNQY+WxjD/PlvceBARYQYkS4QAESiUpXnyy+34O7u7Vwf2FGqVE3Cw7eC3Ot2nEOSkgokCW5eUaiQHzalhpMkkXEL3hagbIm8lWYVKxZEhMWUtkvRns2SROmAR2ex+a/j9GuuJElvA5eBecgh1d60O+2HnHf0VWfbcVEwOEqu+yTQqWZN7hoMzJIkOaN6KIhQmCNJXNHp6FKnDmW8valUojTwGhBnd/U/wESm92gHwNAuXRit0XDarsQ94B2Nhg/atMnzDBVvNa0JvANcsTt6HXiLno2qA/Buq1b8rFRir0kyAh+oVDQNKseIYnsIDGyAm18Qw5XqNHcpkFdDNyvVNGn6FhqNjoqVW/EaEGlX1znkuIiNOsgbeTp2HIhaPQvYa1cqAbW6D88++2qeOkGALl2GotGMhgy9rtG8Q5s2H6BSFWwaNGdQKlW0aj+MNzWGTH0+VGOgVZeHx3bNKQaDJ02a9KKXRp/pWz5Vradlp9FZXeoiBaccoSRJrYGFyJ9xF+Br+/NCiBPIya47O9OOCxcZUSmVbBg1il+KFydIq+WkQmK/QmKJnx8bx4xJc177xw+hrHc48srZMeAg0IzBbRvxRmN5da1VlSpM6tmTplotRxUKjiskAtVq6jZuzOjOef/VXfBmL1pXKQxURA68dAQoT/OK7nz/tvweWdrbm98HD6a/hwf1dDpe1Okoo1ZzJyiIpQMGANBb+oGwoW+yxb8OpTQGuug8qKzzYFjhkgwcGZo2tfj+4PUkl65BGeQIGIeAGkD5Bi/TufPHAJQpU4P+/b9Fr++MQnEYSEKtLkPNmp706uUoPJtzVKnSip49J6HVNkWvb4VO9wJqdSCNG9elc+en/8HdtuMo/Jq/Qzm1jvY6D5rrC1FPY6D1yzOpUaNDnrfXredszLW7UFqto5POg4a6QrTRefByn6WuIODZwFn5xBbgGaCyECJWkqSPgfH20ghJkn4CGgohHGf5fEy45BM5t/1JGQk+TFAvhODg5cuUeXUhOrUGj+0THOr29oWH4919HiqlkiI7J1HIYMhUJtFoJLHFJGw2gdgwAj9Pz0xl8tL2K1FRmFtNAUC1aRRlfX0zlbHabGw/c4a7cXHUKluWoGLFHNY15Vodrl8/iZdXCSpUaOxQO3b9+mk6dmyDJCkICztAoUJFM5WxWEw0b14Xd3cbP/+8Lt/TKhmNiZw6tRWzOZmKFZvh6Zlxci/nPAnyiVRiY+9y9ux2VCoNlSu3TEtC7Ii8sDsq6ioXLvyNTudBpUotUKsdB0nPLv8V+YSzcz51gV+EELEPKXMN+XXcxVPO4YgIFob9zY2YRJoGleStpo0p4u5eoDZdiYri93+O8mrHRAwaE1GRkQQ4cCgNypcHv5QHvwMnmJycTJc5cxgXdB2Ad6ZNY8OQIfg7qOtRRCck8P3O3Ww/e41u/V7j4M0b1PH3z1SujLc3FE2pP4t2lAoFLSo/fE3p9v37HD30B7cuHsDDxx93d+9Muj+z2UhExCFs5mSQFFy+fJCqVdtmcpgqlQZ39yIolTGPJbegVmugVq1/b4zSQoV8qVfv8aVz8/Yujbd33ocO/LeTF0G3Ex5Rxgs5Lq2LpwRH64LjV66lyZT5fLu9AasPv834VVBu2HgOR0QUjJHAqoMHqTR6MrM2lCIqvjjXonVUGT2F5ftyFvIrJj4ev379OX/yJP5AIOBz6xbVhg3jz0OHclTX8atXKTdsPONWWlh9+G3uxrnTbOo3DF++Kkf1ZJcdZ89SdfhwvNZMos+R1dTYNp9ZE+qybdNXaWXi46OYOqYqZ5b2wy8uEp/YO2yY14P5M9tiseTtblAXLp5GnHWEl4E6jyjTADjrZDsuCpA958/z+YY9JJqOYrVNAN4gyfQz95Pm8fxX32Kz2R5VRZ5zLz6enguWkmTaisk6B/BDiPIkmbfz5nc/cTfWwSTFxlEOdYBVx4ylEoIzQKkQKB4iS937AL3mzM1UPiuEELzw1SJiEj8n0bQceAMoTqLpOPO2HiLs9OnMF2VhU3YwWSy8/NVX/M9oZKHZzOvAVJuFf8xJrP11FDdunAFgxZJ+dIiKIMwYT/EQ+R6PGOPxCN/Nlk1fZjZpYxg7dmQ+7sLFvxVnp0b/BIZLkvSiEOLXjCclSXoTqA6McbIdF4+BrNYE527ZTZL5IzJvzn6J+4kT2BMeTpOgoPw2TyZFmJ4UHcOGTVpsYjBgny39QxSSDqPvZChip0N7iLOJvx/DdNKnypWA8cB8YePv8HAaln9UkhU4cOkSd+MUyA7QHh+STEOZveU3gitlDAGQe9YdPUqQzUabDMf9gXetRvaELaBTt8kcPLKGlVZzOjWlGphsSuKVLbNp12FYntnkwsXTiLMjws+Q94AvkyRpOdAQQJKkASm/LwTOA5kS97p4eoiIikOIZxyckZAI4mZM3gqEs4PJYsEmMq/1AdiEG0ZL9rOlm5D3b2bECygE7LtwIVv13IyJQaEojyMBv+AZrt6Ly3yRE9y8f5+KWYzGK9psFI7cQYeEhRgUShyJHyoCMfH38tQmFy6eRpwNsRYtSVJz4AfgRbtTs1P+3Qm8KoR41DqiiwLkUbtD65crzoFLYZitHTOcMWOx7adqqYb5ZlsmUkZ2B/bv553FJ4hLDgNIJ0x317bi616BBDZqlEUl6dEif1F7ZDh+GVl92Ll27WzVU6VkScyW/yEr/tLv1lMpQqnrn7d7xqqWLMkcSXIY9X6HWk3VgAD8ChUChY3zyOHn0pUBSvtlPOrCxX8PpwX1QogrQohgoCbQHxiLLKyvJ4RoLoS47mwbLvKP7EgkPmjVDLVyEbDb7qgVtXIYtcqWoFIJxzny8oS206jZbHymw51r10avOYfEt6RPbvIDOvVxutfN/o7vahUqMBCIADnySqi8A6w34KbRZXvnaHk/P54NDECj/Ij0+8P2oVEtYFCb5tm2KTs0CQrCUKQIUxWKdD2wHlijVPJm8+aoVSrea9WK/hoN9iKOa8AQjYGQFzL3rQsX/zXyLFGVEOKYEGKBEGKqEGKeEOJgXtXtomApV7Qovw94Cw9dRwrpmmPQ9Mag8adG6S388WHBJBLRqFRsH/khpYpMwkNXHYV0BqXiH0p4jSJ05IfoNNmPTLJjzBgUHp5URJbcn0ReDT0iKTkz87Mc2fXbgN7U9t+NQVMGg6Y3Cukc7tp2/NL/jSyTBucWSZL4Y9gw/vDzo5JWSx+NhqY6He+4ubFqyBB8C8l5/cZ26UJg3br4q9W8onHjBa07ldQ66ncaQ926XfPUJhcunkacEtRnWakk+QBNgURgixCiwOUTLkF9ettzI5ZPNplYd+wYkfHx1CpThnrlyuWxhQ7IIqdfKjabjW2nT1P5rR/QqdV47piUdfb5R7Dt1CncX/gSmxDsntuNIe3a5drsg5cvc/DyZYq2aEHb5GT0OXDMOUUIwe7z5zl14wbFPT1pV60aagdh4S7dvcu2U6fQqFTE1pj20LBpT5IoPac8rbY/iXa7BPXZQJKk/sgzSO2FEPdSjtUBNgCp2Uz/kSSphWud8OlHp9HQNQdTjs5y/OpVDHcicfPxZtnevXStUwetOn12b4VCQasqVcDLK/VApnpMFgurDh5ky8kLeBk0vNaoPjXKpBeLCyFw02op4uaOVQjKFy2KxWpFpcxdQtM6/v7U8fcnzGBA72BDixCC0NOnWXvwIEIInqtThxaVKuUqm7gkSTQJCnrkzt0AX1/ebp46PbsWgKX0ynF7Lp5MLBYTBw+u4sLJLWgMXtRv9BplytQoaLOeCpyVT7yEnG3efuvZDKAw8D3yDNNzQD/gcyfbcpEHPClh0x6GEIKhv6zi621/s76JgXKFVfRdcpIRK9by97ghlCyc/fQ8t2JiaDT5C+7GlSTe2B2ldJd52+bxdtM6zH7tRSRJwmaz0XPBD6w+HM5fLTwAJa8t2ElZnw3sGj0QL7esw2LlBqPZTPdZs7hw8SKvG41IwEe7d1OqbFlWDR2ao2ldZ+nFUpcz/BcQE3OLLyY3omTcXbob47krKZm3bR51mr7Ni6/Ndhh20MUDnF0jrIC8rAKkTYk2BxYJId4RQnQCDuDKPuEiB/x19CgLwk6SZD6NoBygJj55Bzdj3ual+T84vigLYfqrC37i6r1XiTfuBj7CKqaSZDrN97su8EdK1Jjvduxg9eF4Ek1nkFV4pYk3HuT87eb0W5pJHus001evRgoP56jRyChgJHDYaER/6RKfrMqfCDQPoxdL6cVT8IbkIkt+WvAqr967ym5jPB8BU4WV06YkLuz6nkOH/iho8554nB0RegN37H5PTZZt/9e8E3n61IWLrEkRygOUqnSDtS+UALplyJZuQyEdJ7ncJHSpU6QPEcpfj47m7/BwLLYdGc4UZu0GBYX030PZTdSocIW/elUAnsuQMd6MQtqLpcJ1VKlTlrnNUG/Hwq1b2WQ2Yz/JqwKmm800CQ1lco8erjd4F9kmOvo64eF/s8OWXjtbGJhgTOCz9TOpU6dLwRj3lODsiPAe4GP3e3PABuyxO5ZVOm4XjxnvqIK2IHskm82Ao+lIhRw1Jpti+evR0WhVpZHztWfEDWNKhnuTJav21EiSCrMl7/Z6WW02biYm4iiMdhBw32RKs+tx4xoZPp1ER1+ntErr8FteFbgXdcXBGRf2ODsiPA10kiRpDLJw6iXgQIZsFP7ALSfbceEEqeuCYQWbKOLh2I20pnyxkLVHXwEGZMiWHodOXYqLMwbjmbo55iGU8/XFaLkM3AfSp1QKCelL6yqL2DSsPx+Om8GRq8OALhkyxl9Dr6lC5JwRoHUunU0qSoWCQE9P9t+/T4MM5w4BxQyGTBuCHje9WMrvlC1QG1xkH1/fcly2GB18y+Ws6MVK5F1Yv38rzo4IvwKKI+tzryKnW5qfelKSJCXQBDkfqAsX2WJ4h+YYNJOBi3ZHbWhUg2lVpSrFs+EEAXw8POhYoxZa1UDSC9wjMGgmMPI5eQfl6E7NcdOOAG7blTGhV79P7yaNMeSRE0zlgw4dGKzRYP+2GA98pNEwoF27J2Ja1Jso1+jwKcHDw4daNToyUKXN8C2HCRoDzZ8bWVCmPTU45QiFEKuRd4SeRM4wMVQI8ZNdkVbI06IbnWnHRe5wlE7pobSdlm6trqBoWrEin77YFq2qBgrpFGDETRtIzdL7+LFPT8cXZWH7ordfoY7/Edy05VArB2LQvIJOXY3J3Vqk5fnrXq8eA1tXQ6cOQiGdQZLOY9CUpXnF28x6Oe/XVga0bk2tZ5+lvFpNP5WK/ioVgWo1z9Sty+AOTmQvz4fPz9np0rZtg2nbNjjvDHLhkFfeXsQR/zqU07oxUKnmFY2BamodLbpNpnLlFgVt3hOPs1OjCCEWIgfXdnRuI/KarQsXOWJA6xa81KAuZr8pqFSCTUN70rB8+RyPlgrp9ewaM4j9Fy+y89w53LVautadQtGUqCsg6/CmdH+e/i2aIBWdjk2Y2T1mADXL5s/0oEKhYO5bbzHouedYe+QIAthZo0aW2eefBFwyiycbvb4Qg8bs4uLF/Zw7txOt1p0pdbtSqFDRgjbtqcBpR+ii4EiITmDvsr1EX4miRNVS1O1eF41O1qDlVC946PJlikRGIgREXbpE3YCAfLA4ZyQYjSisNpQI7iclIYTI5Ajjk5NZvm8fwbXuoteo8UhKwkOfftuAEIKYxERiEhKwWK0kGI0O2ytVpAikaBRL55MTtKe8nx+D2rZ1up6YhATG/P47b1eMQKtWo7l1iwr54FRTR4bZcYg2m42TJzdTu/YlFAold+9ewte34L9T/2YkSSIwsAGBgRlXn108ijyLNZoXSJJUWpKkUEmSTkuSdFKSpIEOygRLknRfkqQjKT//yajBh9ccZlSpjzAOW06DT9dxsf8PjC75ETU/uZIjJ2ixWuk2ZxFNpy7kcqSBiCgDwdMW8fyXCzHnIJVRXjNl9XoqjZ7CpUgVJouSHvO3UGXM1HQJd/ecP0/JQaMY+PMdrt4rxIU7Fkp+NIrtZ86klbkXH0+jceMYOW8eqrVrub5qFfVHj2byypUFcVt5zp+HDlHq/QEc2bYd7+RkRFwc1UeOZMTy5fnW5qOmS+Pj7zF9XA02zOuOe9QV1JGXmTq6MmtWfpxvNrlw4QxPlCMELMAQIUQl4FngfUmSHO003ymEqJnyM+nxmljwRN+IZslLX7Ml0cTyRBNjgc3xycy+l0DnmTOxWLO/3f+zdRvZcFxBoikcKAeUI8EUzpZTeiavXpdft/BQtpw8ydS1e0g2nyI4+AYrVswnPvkIF+48z6vf/AjIo8X2n88nNvlnEoxrgbJYbVWJS/6Njl98Q2xSEgD9Fi6kwa1bHEpOZgIwx2rlhNnM0g0bWHfUwR4uJzLGP25MFguvzZnLXAS7sVIWebv8HmDu+vX8HR6er+1n5Qz/t/B1Wt86x/HkeAKAICE4bU7m4IbPOXq0YL5TLlw8jCfKEQohbgohDqX8Pw5ZnlGyYK168ti9aAcv2mzUy3D8FaCEycT6Y8ccXeaQLzZuJ9H0Bem1dlqSTF8ye8t28iMo+6OYsW4niaZxyBuSU5EwWz9h1/nzXLt3jxX792MTzwIZA2O3RIhglu3dy62YGLacPs0UqzVdvj4/YJzJxNfrnu6H8oz16ykpMkerqAW8hpJRv6zIdxsyjg5jYm5x8vQ2pltNmfp8oimBnetyls3DhYvHwRO7RihJkj/y3/Q+B6cbSpJ0FLiBvFP15GM0rcC5d+oGbYyOpy1rWyxciox8eAUpOwuFgF/X3AY+AsgQxQUk7iGCpiEpUh5pj2mkdP52JFDHwRkDm7dY8Cj9JcGtEljzmgcg25re9iuULnIfg0cYm2tZcbcBIelrqg18eucOTyUpn1+XajfoOsAuoHdwyr+hMBErEaqLUN5uF2k8qBWFAAAaIUlEQVQ+fn692j4DwKUkNxrVMst9nsGmbkCQ6m/8ywenXbdxY1i+2eTCRXZ5Ih2hJEnuwO/AoAzifJB1x2WFEPGSJHUA/iBz8u3UevoCfQF8/HxwzwNFuTJemSf1OEPP5/ugrNeDMAejtaqSRDFfX8IMhkzn4pVKwtzdqWmXUaF8YHkE97CfHAgMjAFsSJQjVv3gK3LE/fHc96iJQ4hNuggpSrtSpeKZOTMMsFE2wA+rVou7Xk+glwBi0l0r265Gp/bFqlDiFxBANIKjM99LVy4K+EinIyyf7ym1z/OS1M/Pp3gJzL7mtB7wStFdxgTK6bFKKxTE2H3WOfn8cmp3qk1ebsmoA/yJRiBlsMkMlFaoUCoffGbu7mHZbiO7KJXx+VJvfvO02v1vIF/yETqDJElq5BwxG4UQs7JR/jJQVwjx0GHQvykfYfSNaCaUH8HmJFO66dFfgFGFCnH+iy8cpg8Kc3cnOD697VPXrGPKmtskmtYTGtoegJCQjeg1zzGsnScTuz6fj3fimC0nT/LCV8tINO0DSjBzZhhDhzZHrRxK84o72Tz8fRKMRkoMHEls8s9AO7uIMONw13bn+pfTKKTX02PWLIqfPMmXdtOjt4EmGg2zBwygffXq+Xovjvo8rzBZLHj36cscYZOnR0Pl44dC5GSgW8aOpWH58rmq2xm7e8yaRdGTZ5hjNSGl2HQ7BJ7VuNF1wK9Ur94+V/Vmlycxr192eBLt/q/kI3yi1ggleW/8IuB0Vk5QkqRiKeWQJKk+8j08JVE084bCJQrz5or3aGXQ0MOgYTLQXqtlmLs7fw4blqMcesM7tKVdNRsGTSBwAbiIm6Y8rSonMfZ5J8TdTtCqShVGd2yMTl0ZjbIfcBMPXU0Ci67m536vA+Cm1bJ+yHsU0r2Km7YjEIFScQIPXXfWftSPQikSim/69mV/8eLU1umYAAxQKqmiVtOrXbt8d4L5jUal4qcPBjAAiYYoiABOIEe+H9C+fa6doLN807cvB4sXpZrOnUvAOUniGbWOOu2G5LsTdOEiNzxRI0JJkpogZ6s4jhy8G2A0UAZACPGNJEkDgP7IO0yTgMFCiD0OqkvHv2lEmMrz8xNYtncv1+/do1LJknSvW/ehuewe9pZ/6PJlinSbI+sIfx/wROgIIyIjWb5/PyVbt6bI6dO0rVo1U+La+ORkVuzfT/DAP9BrNLiHfZxJR2iz2dhy6hQ7zpzBQ6+nR/36BPj6PpZ7yM8RYSqxiYmM+u033p66B61KjWbLGKd1hM7andrnFd5YTKLCk/Xrtz42HeGTOLLKDk+i3f+VEeETtUYohNgFPDR0iBBiLjD38Vj0ZJKqE0xUqfDU64l3c8NTr891NnWA2v7+4CMnEgl4Apwg8P/27jw+qups4PjvSYYECEtAjCBgcGGrIIIoIiCLCiKCrVDFqgjUFyJU0Nq3llotXd7q+9ZdqnVlqVrauuEuVdkpCmpUsLLKIvtiWBOSkOf9496p4+QOGcgsdzLP9/OZzyTnnrn3uQeSk3POPedw6PBhPt24kcalpWzc7d3or1e7NqMvvBDyFjkJdSqvwZ+RkUH/Dh3o36FDPMNNmgZ16/KnESMgf7OT4IMVaoJljvsHx5knzmc6/vh/ZUw4X3WNmui9/8UXNJ14OwUzvuKOF1pw7eOLyL/tTlZtqxkbffxwyuN8747f8fySFuwtrk/B9HepX/BzNuzcmezQzHGyRbyNX1lFmGJumA479u1jyEOPs7/kZQ6UzKa84j72lyxla9GdXPS/UzhSUVH1iXxsyrvv8sKyVThruf8DaAls5FDpFZz3uweTG5ypFtvz0PiRr7pGTXSmLljEkYohfDtJy6EUsLf4z/xz+XIuPZ4HQXyyosrvZs0FJuOsdBOUATzAjn1/pXDDhsoLYvsk9qTyYxlEiOlY1i01Jt6sRZgiQrdUKty4k5Kynp75SssvYNX27Z7HUsWeg8VAd48j9YHT+NfatQmOyMSLtQ6NH1iL0Oe8FtA+I68hWYFPKfVYXCYr8BktG3eJf2BxVK92NkWHllN5dZnDwAY6Nh+chKhMvFjr0CSbtQhT0I29e5Apz+HMGgv1OoHMlQzq1CkZYcXMzRd3Be6k8vTQP1C/dh16tm2bhKhMvNn4oUkWqwh9LNJ2SvlNmvDkqGuoXasH2YExwCPUzRpGg9ojePPWArICqd3Qn/z973NOfg5wOnPmnMJVV40Hzicz4wHev/2mZIdn4swqQ5Noqf0bM41de8H59GnflqkLFrF+1+t0apnH9Rf8ntycnGSHVm0ZGRks+80veHHpUurXfoYMyeYnF9Xj7h/+kXq1ayc7PJMANzDdukpNwlhFGKXi/cV8+sandAt0Y8uKLTQ/M/m7QzVv1IhfDbm8ynwfrV/PR+vXc2K/fhSXllLnKKvP+En/Dh0obpyLBDIp6NfPKsEIVJVFq1ezYvNmTs7NZUDHjinfKwBWGZrESf2flgRY9Owipo+bTkZ+Bq3Ht2Zyr8m06d6GCX+fQHZOdsyvdyw7zB/NngMHGPTAE3y2aTdwMb/vWEzeL2/n+YIbGNy5c2wuEid/WbSYgukzefufmbSq04BzJz9Ejzb5vDLhRnKyY1/mqerrPXvo/8fH2LinAtVeZGZ8QCDzOV67ZSw9WntuypJS7EEakwg2RliFdR+uY/qE6ZReX0rJsBK0oVI2voxV21bx9Ninkx3eUQ2bMo2P1/fkUOkGDpVOo0LbcODw2wx/bAYrt25NdngRfbhuHQXTX+ZQ6WKOVJwF1Ka4bBMLV53B6KefT3Z4vqGqXPLHR1m1/XoOHl7NodKp7C/5F98cnMGl9z7Kzn3hO5ilLhs3NPFkFWEV3rjvDcq6lUFeSGIAyi4p45NZn7BvR/V/2QRbgKFzBatr9bZtLFn7FaVH7gdC1yDtRmn5WB6cPS82F4qDe96YQ3HZJODMkNQsSsoe5dVPPmZHDfoFXx0LV63i6z1wpOJXfHeJ3oEcqRjMU/MWJiu0uLCnSk28WNdoFTZ+vhHt5bFDRx0INA2wffV2GuQ1qPZ1YlUBBn2xZQu1AudQXFa5G7G8oi/L1r8d2wtW14Bvd1L/+RkbmFB2EJgVtvM8BDKU7Jb3fru4th9XU0mQ5Zs3c0QvxGud+uKyfiz96pnEB2VMCrIWYRVym+WC15a/R6B8VzkNmzZMeEzRaJabS0XFGqByJS58ScvG9RMfVJSyAwGcHbbCKRVaQlam/f0G0KxhQwIZX3oeC2R8QX6T6v+B5kfWMjSxZr9RqtD/pv5suHUDpW1LIaRxJR8Kzds3J+/0vMgfjkKsW4JB5556KifWr+DA4RnwnQcNdlEn614mXPyj+Fz4eIW07NYuXcrIJ9/nYOlrzJkzmNNPL6Jv37lkyH10zt/Dssk/S2Kg/nFZp05kZjwPzAb6hxxZT63Mpxjbp2aXkz1VamLFWoRV6Dq0K90u60bWM1nIIoFDkP1SNvWX12fcs+OSHV5EIsKsiT8mt+5t1M26GpgBbKVuVkfGX9SFPu3bJzvEiIZ27crV3VqQk9UB2AiUkZM9mCb17+Xv40YkO7zoDbibsy+8K26nzwoEmDVxDPWyh1O71ijgLwQyJlGnVlfuuepy2p18ctyu7RfWOjSx4Ksd6uOpOjvUqyprFq9h/oz5XNPtGpYcXkL367pTp37lTWCjFa+WYLhvDh5k2sLFzPtyI1cWXM+ZW7dwTqtWibl4Nagqi9es4YShj9Iwrwmv/LI713XvXmn3eV8bcDdFmZnkvvnzuF5m+969PDlvIUu/2s6pTeoztm8P2lezEqzuDvXJMp0bfLnTezT8GLftUG/+Q0Ro3aM1rXu0pt7cevTr0++4z5WoCjCoUU4Otw64hFsHOL/czslOjcn0IuLMg2uWR1FmJjf1O/4yT4b3VqzglLN30DAvj5LSUmof5yIGRyoqmPvll+zav5/O+fm08dh9/qSGDfnVkEHVDblGuIHpvEh+1RmNCWEVoTExtOWbb7jgzsnsObCXhZdB3caNaDJmLL8dfjU/vfTSYzrXgpUrGTrlGUrKmgKtKD/yD3q0PpUXbx5Ng1RqGSfYCexmqI0fmmNgY4TGxFCPu35D9wP72QGcBeQAr6DcOXMm81eujPo8m3bv5rL7/8zO/VPZX1LI/pJXKC7bzIJVHRk2ZWq8wjcmLVlFmECJ7hatEd6ZROH83yY7iqgsXLmSnfuLeJoKQldFvRj4MRn87NnoV8WZ8u48SsuvBQaGpGZxuPxRFq5ay+pt22IUdc1lD9KYaFnXqDHV5S4GcHKXXSzoD3WD6X0gl3UwByZTwZeyCdp+u3DA0RYDWLRmC6VHbvQ4kk2tQHcKN26ktcd4oanM1is1VbEWYQLEcuk041+1awUowWsJA2d5gMyM6H/cmuXmAOs8j6mu58T6/l0Qwa+sdWgisRahMdXltuzyystp919jmKoVDAWYA0Wnn0ZG33Wcj1Aw9Aq6DR4c1SnH9Tuftz57gIOHRwCNQo68Tt2s3fRq2zbWd5EWrHVovFiLMI6sJZheAoEAD40exQhgNBnsAkqB9ghNmjVn0qDopzj0adeOUT07kJN1NsKDwCyyAwXUyx7BSzePPqbWpanMxg9NKGsRxolVgOlpVK9edD7lFG599nlWZ6zmFBF+evVV3DpgABnHUHmJCA9fN4xh53bk0fdfYWvRIXq2ac74i+6ieaNGVZ/ARMWWaTNgFaExMXd2fj5z7pgEbZyVZW4bOLDqD3kQEXq3a0fvdu1iHKEJZd2lxirCGLOWoDGpySrE9GUDDcYYE8LGDtOPtQhjxFqCppJ3JlFYrx59SL3Fq9OdtQ7Ti7UIY8AqQWNqJnu6ND1YRWiMMSatWUVojDFVsFZhzWZjhNVgXaLGpA8bN6y5rEVojDHHwMYNax6rCI9Dqi2dtvfQIZasWcPhsrJkh2JMjWGVYc1hFWENVlZezrjpf6PpxNu59L7X+GLLDjrccTefb9qU7NCMqRGsdVgz+KoiFJGWIjJHRP4tIitEZKJHHhGRh0VkjYh8JiJdEhnjCbsTebXqGTNtJtMWlVJStpq9xYVU6Fms2Pwzev3hAbYVFSU7PGNqDKsQU5uvKkKgHLhNVdsD5wPjReR7YXkGAq3d1xjgsUQElmrdoduKivjrkg8oLn0BOCnkyI0cLr+SP703L1mhGVNjWWWYmnxVEarqVlX92P16P/BvoHlYtiuAGepYAuSKSLMEh+p7S7/6iuxa5wG5lY6VlP2Ad5avT3hMxqQDax2mHlH12k87+USkFTAf6KCq+0LSXwfuUdWF7vfvAber6jKPc4zBaTUCtAVWxiC0JsCuGJwnGVI19lSNG1I39lSNG1I3dj/Gna+qJyY7iHjz5TxCEakHvAjcEloJBg97fMSzNlfVJ4AnYhzbMlXtGstzJkqqxp6qcUPqxp6qcUPqxp6qcdcEvuoaBRCRWjiV4HOq+pJHlq+BliHftwC2JCI2Y4wxNY+vKkIREeBp4N+qen+EbK8CI9ynR88H9qrq1oQFaYwxpkbxW9doD+B64HMRKXTTfgmcAqCqfwbeBC4D1gCHgFEJjjGmXa0Jlqqxp2rckLqxp2rckLqxp2rcKc+3D8sYY4wxieCrrlFjjDEm0awiNMYYk9asIjwKEckUkU/cuYvhx0aKyE4RKXRfNyYjRi8isl5EPnfj8ppfmdRl6iKJIu4+IrI3pMzvSkacXkQkV0ReEJEv3SUCu4cd92uZVxW3L8tcRNqGxFQoIvtE5JawPL4r8yjj9mWZ12R+e1jGbybirG7TIMLxv6nqTxIYz7Hoq6qRJueGLlPXDWeZum6JCqwKR4sbYIGqXp6waKL3EPC2qg4TkSygbthxv5Z5VXGDD8tcVVcCZ4PzByuwGXg5LJvvyjzKuMGHZV6TWYswAhFpAQwCnkp2LHFgy9TFkIg0AC7EmfqDqpaqaviq5r4r8yjjTgUXAWtVdUNYuu/KPEykuE2CWUUY2YPAz4GKo+QZ6na5vCAiLY+SL9EUmC0iH7nLzIVrDoTuxfQ1ldd0TYaq4gboLiKfishbInJmIoM7itOAncBUtyv9KRHJCcvjxzKPJm7wZ5mHGg781SPdj2UeKlLc4P8yr1GsIvQgIpcDO1T1o6Nkew1opapnAe+Cr1bZ7aGqXXC6hsaLyIVhx6Nepi7Bqor7Y5y1DzsBjwCvJDrACAJAF+AxVe0MHAR+EZbHj2UeTdx+LXMA3O7cIcA/vA57pCW7zIEq4/Z1mddEVhF66wEMEZH1wEygn4g8G5pBVXer6mH32yeBcxIbYmSqusV934Ez/nBeWBZfLlNXVdyquk9VD7hfvwnUEpEmCQ+0sq+Br1X1A/f7F3AqmPA8fivzKuP2cZkHDQQ+VtXtHsf8WOZBEeNOgTKvcawi9KCqk1S1haq2wum+eF9VrwvNEzbWMATnoZqkE5EcEakf/BroDywPy+a7ZeqiiVtEmoqIuF+fh/P/N+lbJavqNmCTiLR1ky4CvgjL5rsyjyZuv5Z5iGuI3L3ouzIPETHuFCjzGseeGj0GIvJbYJmqvgpMEJEhOJsJ7wFGJjO2ECcBL7s/RwHgeVV9W0QKwDfL1HmJJu5hwE0iUg4UA8PVP0sj3Qw853Z5rQNGpUCZQ9Vx+7bMRaQucAkwNiTN92UeRdy+LfOaypZYM8YYk9asa9QYY0xas4rQGGNMWrOK0BhjTFqzitAYY0xas4rQGGNMWrOK0JgYcncOUBGZHGX+kW7+kfGNLP5EpJV7L9OSHYsxx8IqQmNM1NyKbm6y4zAmlqwiNMYYk9asIjTGGJPWrCI0viMiQ0TkPRHZKiKHRWSLiMwTkXEeeRuLyN3i7K5e7O7s/Z6I9PfI+5/xOBEZJCKLReSgiHzjbqXV2uMzbUTkHhFZJiI73Xg2iMgT4uxZGTci0kJEpojIOve6u0XkVRE51yPvZPfe+ojIMBH5UEQOicgeEZkpIp7bD4nIuSIyW0T2i7Nb+rsi0j30fG6+kSISXIaqt3ss+Jrscd5W7nV3iUiJW3620azxJVtr1PiKOPsQPg5sw9nqaheQB5yFs1bkoyF584G5QCtgAfA2kANcDrwtImNV9UmPy1yJs/r/y+7nzwaGAn1F5AJ3F/HQvAXAHGAxUAqcCdwIDBaRrqq6OQa3/h0i0gWYDTQG3gFeApoA3wcWisgP3J0Jwo3DWQT+VWAezo7sVwOdROTskB1TEJFe7jVqAS8Ca4GO7r2+H3beQuA3wK+BDcC0kGNzw/LmAx/irF36F/cergZmicjFqjon2nIwJiFU1V728s0L+Ag4DOR5HGsS9v1cnI2Th4el5+L84i4GTgpJH4mzH50Cl4d9ZqKb/l5YenMg2yOW/sARnL38QtP7uOeZHOX9BmMaGZIWwFkougToHZb/ZGAzsDU0LmCye559QMewzzzvHrsqJC0DWO2mDwzLXxBSTn3CjikwN8K9tAr53K/Djg1w099M9v8xe9kr/GVdo8aPyoGy8ERV3RX8WkQ6Ab2BF1V1Zli+IpyWS22cll6491X19bC0KTgton5uSzN4rs0a0ooKSZ8NrMD5BR9rg4DTgUdUdV7YdbcA/wc0xdk2KdzDqvp5WFqwVRy6v+MFwBnAHFV9Kyz/E8Cq44wdnBbj70MTVPUdYCOV98Y0Jumsa9T4zXPAfcAKEfkbTvfeIlXdGZavu/veMMKcvRPd9/Yex+aFJ6jqERFZiFMBdcb5ZY67L9y1OC23TkAjIDPko6VV39IxC95bfoR7C45ltsfZaijUMo/8m9z3RiFpnd33heGZVbVCRBYDbaKKtrJCVT0SIY7uHunGJJVVhMZXVPV+EdmFM9Y1AbgFUBGZB/y3qgZ/0Z/gvl/iviKp55HmtZs5OOOSAA1D0u53Y9iKM1a3GafLFZzKMZ/YC97bD6vI53VvRR5p5e57aAUevMdIZREpPRpeMQTjsF4o4ztWERrfUdUZwAwRycXpwvsBMBp4R0Taq+oOYK+bfaKqPnyMlzgpQnpT930vgIjk4VTGy4ELVHV/aGYRueYYrxut4L1doc4m0PGwz32PVBaR0o2pceyvM+Nbqlqkqm+q6n/hPKXYGOjlHl7ivvfy+mwVeocniEgm0NP99hP3/TScn5HZHpVgC/d4PFTn3qIVvMee4QdEJAPnDxAvFXy3ZWlMyrOK0PiKiFwqIl49FXnu+yEAt4t0AXCliIyOcK6ObqsuXD+POW0/wRkfnKOqG9y09e57T7eiDJ63Hs4DKPHqUZmF8+DOeBG5zCuDO9evbjWusci9Rl8RGRh2bAyRxwd3Ay2rcV1jfMe6Ro3fzARK3AdX1gOC0zI6F2dqxbsheX+EM9/taRGZAHyAMz7VAmfeYQechzN2hF3jNeBlEXkZZ5pCJ+AyYA/O2CQAqrpNRGYCw4FCEZmNM7Z2Cc7UhkKcOYgxpaplInIlzpjkG+6DK4U4fwS0xCmL04BmbtrxXKNCRG7EmXv5qogE5xGehXN/b+HMtawI++h7wHAReQ3n36McmK+q848nDmP8wCpC4ze/wJmS0AWncirBeYLzdpw5e/+ZVqGqX4vIOcDNONMkrsXpttsGfAE8AoRPJQBncvoTwB04UxXK3LRJqho+beDHOBPDrwbGAztxJqvfhTMJPS5U9TN3ishPcRYIGIVTKW3F6db8Nc5iA9W5xlwR6Y0z1WGQm/wB0BenLOHbscSg4HzLi3D+fTJwJtpbRWhSlqhq1bmMqQHE2epoKjBKVaclNxp/E5FFOKvSNFTVg8mOx5h4sjFCY9KUiNR1n8wNTx+J87DMbKsETTqwrlFj0tcpwCci8k+csdIAzkT7njhjrbclMTZjEsYqQmPS13aclXx644wLZuOMr04F/kdV1yYxNmMSxsYIjTHGpDUbIzTGGJPWrCI0xhiT1qwiNMYYk9asIjTGGJPWrCI0xhiT1v4fgd1BcpLUTNwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5.模型使用\n",
    "draw(clf,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
